load packages

# outlier function for descriptive graphs
is_outlier <- function(x) {
  return(x < quantile(x, 0.25) - 1.5 * IQR(x) | x > quantile(x, 0.75) + 1.5 * IQR(x))}

# elbow finder for number of nzero coefficients
elbow_finder <- function(x_values, y_values) {
  # Max values to create line
  max_x_x <- max(x_values)
  max_x_y <- y_values[which.max(x_values)]
  max_y_y <- max(y_values)
  max_y_x <- x_values[which.max(y_values)]
  max_df <- data.frame(x = c(max_y_x, max_x_x), y = c(max_y_y, max_x_y))
  
  # Creating straight line between the max values
  fit <- lm(max_df$y ~ max_df$x)
  
  # Distance from point to line
  distances <- c()
  for(i in 1:length(x_values)) {
    distances <- c(distances, abs(coef(fit)[2]*x_values[i] - y_values[i] + coef(fit)[1]) / sqrt(coef(fit)[2]^2 + 1^2))
  }
  
  # Max distance point
  x_max_dist <- x_values[which.max(distances)]
  y_max_dist <- y_values[which.max(distances)]
  
  return(c(x_max_dist, y_max_dist))
}
options(scipen=999)
writeLines(capture.output(sessionInfo()), "sessionInfo.txt")

Load saved data to start from here

Note that the working directory is the directory where the Script is located

Here I provide the prepared Data:

load(file= "InputData/ClockCalculationsInput/Data_CVS_ITU.Rdata")
load(file= "InputData/ClockCalculationsInput/Data_Cord_ITU.Rdata")
load(file= "InputData/ClockCalculationsInput/Data_Placenta_ITU.Rdata")
load(file="InputData/ClockCalculationsInput/Data_Full_ITU.Rdata") # data persons with all measurement points available
load(file="InputData/ClockCalculationsInput/Data_Cord_Placenta_ITU.Rdata")
load(file="InputData/ClockCalculationsInput/Data_CVS_Placenta_ITU.Rdata")
load(file="InputData/ClockCalculationsInput/Data_CVS_Cord_ITU.Rdata")
load(file="InputData/ClockCalculationsInput/Data_ITU_all.Rdata") # all persons together in one data frame

load(file= "InputData/ClockCalculationsInput/Data_Placenta_male_ITU.Rdata")
load(file= "InputData/ClockCalculationsInput/Data_Placenta_female_ITU.Rdata")

load(file="InputData/ClockCalculationsInput/Data_PREDO_450Kcord.Rdata")
load(file="InputData/ClockCalculationsInput/Data_PREDO_EPICcord.Rdata")
load(file="InputData/ClockCalculationsInput/Data_PREDO_EPICplacenta.Rdata")
load(file="InputData/ClockCalculationsInput/Data_PREDO_EPIC_Cord_Placenta.Rdata")
load(file="InputData/ClockCalculationsInput/Data_PREDO_EPIC_all.Rdata") # all persons with EPIC data together in one data frame

load(file="InputData/ClockCalculationsInput/Data_PREDO_Placenta_male.Rdata")
load(file="InputData/ClockCalculationsInput/Data_PREDO_Placenta_female.Rdata")

This is how I calculated measures of age acceleration/deceleration:

  • EAAR <- as.numeric(residuals(lm(DNAmGA_Lee ~ Gestational_Age_Weeks + Trophoblasts + Stromal + Hofbauer + Endothelial + nRBC + Syncytiotrophoblast + PC1_ethnicity + PC2_ethnicity, data=X, na.action=na.exclude)))
    = a positive value means acceleration, a negative value deceleration

Sample overview

to the top

General Comments

note on the influence of missing CpGs:

  • for the clock of placenta (Lee): not all CpGs included in the clock would have been included after our QC, however they were used here because they are needed for the clock (discussed with Steve Horvath).

  • for the clock of placenta (Mayne): not all CpGs of the clock are available, because the clock was again trained on 450K/27K data. Although the authors here did not report the comparability between the reduced and full clock, we excluded the 5 missing CpGs (that are in the clock, but not in our data) and predicted age.

  • for the clock of Bohlin et al. (cordblood), 8 CpGs are missing in the EPIC data (clock designed on Illumina 450K/27K/CHARM data). Again, the authors did not report a correlation between a reduced and full clock.

  • for the clock of Knight et al. (cordblood), 6 CpGs are missing in the EPIC data, because the clock was designed on Illumina 450K/27K data. Here, Knight et al. claimed that the clock would work anyways (tested correlation between estimates from reduced predictor and full predictor).

  • the correlation between the estimated DNAmGA of the full and reduced Bohlin clock is r= .99 p < 2.2e-16 (tested with PREDO 450K)

  • the mean of the weights of the missing CpGS of the Bohlin clock is -2.159

  • the reported correlation between the estimated DNAmGA of the full and reduced Knight clock is r=.995

  • in our data the correlation is r=.97 p < 2.2e-16

  • the estimation from the reduced clock is again on average higher than the estimation from the full clock

  • the mean of the weights of the missing CpGS of the Bohlin clock is -0.767
    -> overall, both the reduced and full clock come to quite similar results, but the mean DNAm GA estimate differs (account for by using residuals)

McEwen et al. (2018) tested if the 19 CpGs from the Horvath and the 6 CpGs from the Hannum Clock missing on the EPIC array have a great impact on the performance of the Clocks. They had data from both 450K and EPIC. Additionally, they tested the influence of different preprocessing strategies.

https://pubmed.ncbi.nlm.nih.gov/30326963/

Dhingra et al. (2019) also evaluated the influence of missing CpGs of the Horvath clock by comparing 450K/EPIC data.

https://pubmed.ncbi.nlm.nih.gov/31002714/

In summary, it is better to use age-adjusted residuals as a measure of age acceleration/deceleration, compared to the raw difference between estimated and chronological age.

Data Preparation

CVS, data preparation for models

regression input

# EAAR, without alcohol
Reg_Input_Data_CVS_ITU_EAAR_n <- Data_CVS_ITU[, c("EAAR_Lee", "Child_Sex", "Gestational_Age_Weeks", "Maternal_Age_Years", "smoking_dichotom", "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders")]

# EAAR, with alcohol
Reg_Input_Data_CVS_ITU_EAAR_wa <- Data_CVS_ITU[, c("EAAR_Lee", "Child_Sex", "Gestational_Age_Weeks", "Maternal_Age_Years", "smoking_dichotom", "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders", "maternal_alcohol_use")]
sapply(Reg_Input_Data_CVS_ITU_EAAR_n, function(x) sum(is.na(x)))
                                   EAAR_Lee                                   Child_Sex 
                                         64                                           0 
                      Gestational_Age_Weeks                          Maternal_Age_Years 
                                          0                                           0 
                           smoking_dichotom                      Delivery_mode_dichotom 
                                          2                                           0 
Maternal_Body_Mass_Index_in_Early_Pregnancy                          Child_Birth_Weight 
                                          0                                           0 
                         Child_Birth_Length           Child_Head_Circumference_At_Birth 
                                          2                                           5 
                            Parity_dichotom                              Induced_Labour 
                                          0                                           0 
             Maternal_Hypertension_dichotom                  Maternal_Diabetes_dichotom 
                                          0                                           0 
                  Maternal_Mental_Disorders 
                                          1 
sapply(Reg_Input_Data_CVS_ITU_EAAR_wa, function(x) sum(is.na(x)))
                                   EAAR_Lee                                   Child_Sex 
                                         64                                           0 
                      Gestational_Age_Weeks                          Maternal_Age_Years 
                                          0                                           0 
                           smoking_dichotom                      Delivery_mode_dichotom 
                                          2                                           0 
Maternal_Body_Mass_Index_in_Early_Pregnancy                          Child_Birth_Weight 
                                          0                                           0 
                         Child_Birth_Length           Child_Head_Circumference_At_Birth 
                                          2                                           5 
                            Parity_dichotom                              Induced_Labour 
                                          0                                           0 
             Maternal_Hypertension_dichotom                  Maternal_Diabetes_dichotom 
                                          0                                           0 
                  Maternal_Mental_Disorders                        maternal_alcohol_use 
                                          1                                          97 

data frame without missings

Reg_Input_Data_CVS_ITU_EAAR_n_noNa <- na.omit(Reg_Input_Data_CVS_ITU_EAAR_n) 
dim(Reg_Input_Data_CVS_ITU_EAAR_n_noNa)
[1] 195  15
Reg_Input_Data_CVS_ITU_EAAR_wa_noNa <- na.omit(Reg_Input_Data_CVS_ITU_EAAR_wa) 
dim(Reg_Input_Data_CVS_ITU_EAAR_wa_noNa)
[1] 133  16
skimr::skim(Reg_Input_Data_CVS_ITU_EAAR_n_noNa)
── Data Summary ────────────────────────
                           Values                      
Name                       Reg_Input_Data_CVS_ITU_EA...
Number of rows             195                         
Number of columns          15                          
_______________________                                
Column type frequency:                                 
  factor                   8                           
  numeric                  7                           
________________________                               
Group variables            None                        

── Variable type: factor ────────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable                  n_missing complete_rate ordered n_unique top_counts       
1 Child_Sex                              0             1 FALSE          2 mal: 98, fem: 97 
2 smoking_dichotom                       0             1 FALSE          2 no: 173, yes: 22 
3 Delivery_mode_dichotom                 0             1 FALSE          2 una: 136, aid: 59
4 Parity_dichotom                        0             1 FALSE          2 giv: 116, no : 79
5 Induced_Labour                         0             1 FALSE          2 no: 149, yes: 46 
6 Maternal_Hypertension_dichotom         0             1 FALSE          2 no : 179, hyp: 16
7 Maternal_Diabetes_dichotom             0             1 FALSE          2 no : 150, dia: 45
8 Maternal_Mental_Disorders              0             1 FALSE          2 No: 175, Yes: 20 

── Variable type: numeric ───────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable                               n_missing complete_rate      mean      sd      p0      p25       p50      p75
1 EAAR_Lee                                            0             1   -0.0212   0.940   -2.17   -0.685    0.0923    0.591
2 Gestational_Age_Weeks                               0             1   40.0      1.61    29      39.3     40        41.1  
3 Maternal_Age_Years                                  0             1   35.5      5.51    21.1    31.6     35.6      39.8  
4 Maternal_Body_Mass_Index_in_Early_Pregnancy         0             1   24.4      4.21    18.1    21.7     23.4      26.0  
5 Child_Birth_Weight                                  0             1 3519.     529.    1415    3175     3560      3858.   
6 Child_Birth_Length                                  0             1   50.1      2.21    40      49       50        52    
7 Child_Head_Circumference_At_Birth                   0             1   35.1      1.76    26      34       35        36    
     p100 hist 
1    2.90 ▂▆▇▂▁
2   42.4  ▁▁▁▅▇
3   45.4  ▂▃▇▇▆
4   41.5  ▇▇▂▁▁
5 4660    ▁▁▆▇▃
6   56    ▁▁▅▇▂
7   39.5  ▁▁▅▇▃
save(Reg_Input_Data_CVS_ITU_EAAR_n_noNa, file="InputData/ClockCalculationsInput/Reg_Input_Data_CVS_ITU_EAAR_n_noNa.Rdata")
save(Reg_Input_Data_CVS_ITU_EAAR_wa_noNa, file="InputData/ClockCalculationsInput/Reg_Input_Data_CVS_ITU_EAAR_wa_noNa.Rdata")

to the top

Cord blood, data preparation for models

regression input

# EAAR without alcohol
Reg_Input_Data_Cord_ITU_EAAR_n <- Data_Cord_ITU[, c("EAAR_Bohlin", "Child_Sex", "Maternal_Age_Years", "smoking_dichotom", "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders")]

# EAAR with alcohol
Reg_Input_Data_Cord_ITU_EAAR_wa <- Data_Cord_ITU[, c("EAAR_Bohlin", "Child_Sex", "Maternal_Age_Years", "smoking_dichotom",  "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders", "maternal_alcohol_use")]
sapply(Data_Cord_ITU, function(x) sum(is.na(x)))
                                                            Sample_Name 
                                                                      0 
                                                                arrayid 
                                                                      0 
                                                                   CD8T 
                                                                      0 
                                                                   CD4T 
                                                                      0 
                                                                     NK 
                                                                      0 
                                                                  Bcell 
                                                                      0 
                                                                   Mono 
                                                                      0 
                                                                   Gran 
                                                                      0 
                                                                   nRBC 
                                                                      0 
                                                          caseVScontrol 
                                                                      0 
                                                               Warnings 
                                                                      0 
                                                     Maternal_Age_Years 
                                                                      0 
                                                                 Parity 
                                                                      0 
                                                      Mother_Cohabiting 
                                                                     54 
                                        Maternal_Hypertensive_Disorders 
                                                                      0 
                                            Maternal_Diabetes_Disorders 
                                                                      0 
                                              Maternal_Mental_Disorders 
                                                                      0 
                                      Maternal_Smoking_During_Pregnancy 
                                                                      0 
                     Maternal_Corticosteroid_Treatment_during_Pregnancy 
                                                                      0 
                                          Betamethasone_Number_of_Doses 
                                                                      1 
                     Gestational_Weeks_at_First_Betamethasone_Treatment 
                                                                    417 
                      Gestational_Weeks_at_Last_Betamethasone_Treatment 
                                                                    417 
                                     Maternal_Weight_In_Early_Pregnancy 
                                                                      0 
                                                        Maternal_Height 
                                                                      0 
                                                 Maternal_Height_Meters 
                                                                      0 
                            Maternal_Body_Mass_Index_in_Early_Pregnancy 
                                                                      0 
                Maternal_Body_Mass_Index_in_Early_Pregnancy_4categories 
                                                                      0 
                            Weeks_of_Gestation_at_First_Antenatal_Visit 
                                                                      2 
                                       Maternal_Weight_End_of_Pregnancy 
                                                                      3 
                 Gestational_Weeks_At_EndOfPregnancy_Weight_Measurement 
                                                                     13 
                                                       Child_Birth_Year 
                                                                      0 
                                                              Child_Sex 
                                                                      0 
                                                  Gestational_Age_Weeks 
                                                                      0 
                                                   Gestational_Age_Days 
                                                                      0 
                                                     Child_Birth_Weight 
                                                                      0 
                                                     Child_Birth_Length 
                                                                      4 
                                      Child_Head_Circumference_At_Birth 
                                                                      9 
                                                 Placental_Weight_Grams 
                                                                     11 
                                                 Child_Born_DeadorAlive 
                                                                      0 
                                                         Induced_Labour 
                                                                      0 
                                              Child_Apgar_Score_1Minute 
                                                                      1 
                                             Child_Apgar_Score_5Minutes 
                                                                    133 
                                                    Child_NeonatalDeath 
                                                                    426 
                                                         SingletonBirth 
                                                                      0 
                                                         NICU_Treatment 
                                                                      0 
                                                               Asphyxia 
                                                                      0 
                                                      Caesarian_Section 
                                                                      0 
                                                          Delivery_mode 
                                                                      0 
                                                 Delivery_mode_dichotom 
                                                                      0 
                                                        Parity_dichotom 
                                                                      0 
                                                       smoking_dichotom 
                                                                      0 
                                             Maternal_Diabetes_dichotom 
                                                                      0 
                                          Maternal_Hypertension_3levels 
                                                                      0 
                                         Maternal_Hypertension_dichotom 
                                                                      0 
                                                    gestage_at_CVS_days 
                                                                    351 
                                                   gestage_at_CVS_weeks 
                                                                    351 
                                                                preterm 
                                                                      0 
                                                   maternal_alcohol_use 
                                                                     21 
                                  TimeDifferencePlacenta_birth_sampling 
                                                                     57 
                                                              education 
                                                                     18 
                                              education_with_imputation 
                                                                     13 
                                                     maternal_education 
                                                                     18 
                                                        t1_gestageweeks 
                                                                    115 
                                                        t2_gestageweeks 
                                                                    104 
                                                        t3_gestageweeks 
                                                                     92 
                                                             Cesd_trim1 
                                                                    118 
                                                             Cesd_trim2 
                                                                    108 
                                                             Cesd_trim3 
                                                                     94 
                                                   state_anxtotal_trim1 
                                                                    119 
                                                   state_anxtotal_trim2 
                                                                    111 
                                                   state_anxtotal_trim3 
                                                                     95 
                                                              mean_cesd 
                                                                     65 
                                                              mean_stai 
                                                                     66 
                                                          PC1_ethnicity 
                                                                     31 
                                                          PC2_ethnicity 
                                                                     31 
                                                          PC3_ethnicity 
                                                                     31 
                                      ASQ_agespecificquestionnairegroup 
                                                                     85 
                                   ChildAge_ASQ_months_final_30pr_range 
                                                                     89 
         Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange 
                                                                     90 
         Child_ASQ_problemsolving_development_infancy_sum_finalagerange 
                                                                     92 
              Child_ASQ_finemotor_development_infancy_sum_finalagerange 
                                                                     89 
             Child_ASQ_grossmotor_development_infancy_sum_finalagerange 
                                                                     89 
              Child_ASQ_communication_develop_infancy_sum_finalagerange 
                                                                     89 
       Child_ASQ_totaldevelopmentalmilestones_Infancy_Sum_finalagerange 
                                                                     92 
  Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_scaled 
                                                                     90 
  Child_ASQ_problemsolving_development_infancy_sum_finalagerange_scaled 
                                                                     92 
       Child_ASQ_finemotor_development_infancy_sum_finalagerange_scaled 
                                                                     89 
      Child_ASQ_grossmotor_development_infancy_sum_finalagerange_scaled 
                                                                     89 
       Child_ASQ_communication_develop_infancy_sum_finalagerange_scaled 
                                                                     89 
Child_ASQ_totaldevelopmentalmilestones_Infancy_Sum_finalagerange_scaled 
                                                                     92 
     Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_cat 
                                                                     90 
     Child_ASQ_problemsolving_development_infancy_sum_finalagerange_cat 
                                                                     92 
          Child_ASQ_finemotor_development_infancy_sum_finalagerange_cat 
                                                                     89 
         Child_ASQ_grossmotor_development_infancy_sum_finalagerange_cat 
                                                                     89 
          Child_ASQ_communication_develop_infancy_sum_finalagerange_cat 
                                                                     89 
   Child_ASQ_totaldevelopmentalmilestones_Infancy_Sum_finalagerange_cat 
                                                                     92 
                                                          delayed_count 
                                                                     92 
                                                          DNAmGA_Knight 
                                                                      0 
                                                          DNAmGA_Bohlin 
                                                                      0 
                                                         DNAmGA_Haftorn 
                                                                      0 
                                                            EAAR_Bohlin 
                                                                     31 
                                                            EAAR_Knight 
                                                                     31 
                                                           EAAR_Haftorn 
                                                                     31 
                                                           delta_Bohlin 
                                                                      0 
                                                           delta_Knight 
                                                                      0 
                                                          delta_Haftorn 
                                                                      0 
                                                          zdelta_Bohlin 
                                                                      0 
                                                          zdelta_Knight 
                                                                      0 
                                                         zdelta_Haftorn 
                                                                      0 

data frame without missings

Reg_Input_Data_Cord_ITU_EAAR_noNa_n <- na.omit(Reg_Input_Data_Cord_ITU_EAAR_n) 
dim(Reg_Input_Data_Cord_ITU_EAAR_noNa_n)
[1] 385  14
Reg_Input_Data_Cord_ITU_EAAR_noNa_wa <- na.omit(Reg_Input_Data_Cord_ITU_EAAR_wa) 
dim(Reg_Input_Data_Cord_ITU_EAAR_noNa_wa)
[1] 367  15
skimr::skim(Reg_Input_Data_Cord_ITU_EAAR_noNa_n)
── Data Summary ────────────────────────
                           Values                      
Name                       Reg_Input_Data_Cord_ITU_E...
Number of rows             385                         
Number of columns          14                          
_______________________                                
Column type frequency:                                 
  factor                   8                           
  numeric                  6                           
________________________                               
Group variables            None                        

── Variable type: factor ────────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable                  n_missing complete_rate ordered n_unique top_counts        
1 Child_Sex                              0             1 FALSE          2 fem: 193, mal: 192
2 smoking_dichotom                       0             1 FALSE          2 no: 369, yes: 16  
3 Delivery_mode_dichotom                 0             1 FALSE          2 una: 271, aid: 114
4 Parity_dichotom                        0             1 FALSE          2 no : 209, giv: 176
5 Induced_Labour                         0             1 FALSE          2 no: 285, yes: 100 
6 Maternal_Hypertension_dichotom         0             1 FALSE          2 no : 361, hyp: 24 
7 Maternal_Diabetes_dichotom             0             1 FALSE          2 no : 298, dia: 87 
8 Maternal_Mental_Disorders              0             1 FALSE          2 No: 341, Yes: 44  

── Variable type: numeric ───────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable                               n_missing complete_rate        mean      sd      p0      p25       p50      p75
1 EAAR_Bohlin                                         0             1   -0.000994   0.488   -1.53   -0.311   -0.0209    0.311
2 Maternal_Age_Years                                  0             1   34.7        4.70    20.3    31.5     34.3      38.0  
3 Maternal_Body_Mass_Index_in_Early_Pregnancy         0             1   23.9        4.06    16.3    21.2     22.9      25.7  
4 Child_Birth_Weight                                  0             1 3537.       492.    1140    3260     3570      3820    
5 Child_Birth_Length                                  0             1   50.2        2.20    38      49       50        51    
6 Child_Head_Circumference_At_Birth                   0             1   35.1        1.52    26      34       35        36    
     p100 hist 
1    1.26 ▁▂▇▅▂
2   49.5  ▁▅▇▅▁
3   51.0  ▇▅▁▁▁
4 4660    ▁▁▃▇▂
5   57    ▁▁▃▇▁
6   40    ▁▁▃▇▁
save(Reg_Input_Data_Cord_ITU_EAAR_noNa_wa, file="InputData/ClockCalculationsInput/Reg_Input_Data_Cord_ITU_EAAR_noNa_wa.Rdata")
save(Reg_Input_Data_Cord_ITU_EAAR_noNa_n, file="InputData/ClockCalculationsInput/Reg_Input_Data_Cord_ITU_EAAR_noNa_n.Rdata")

to the top

Placenta, data preparation for model

regression input

# without alcohol
Reg_Input_Data_Placenta_ITU_EAAR_n <- Data_Placenta_ITU[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_Years", "smoking_dichotom",  "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders")]

# with alcohol
Reg_Input_Data_Placenta_ITU_EAAR_wa <- Data_Placenta_ITU[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_Years", "smoking_dichotom",  "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders", "maternal_alcohol_use")]
# for split by sex
# with alcohol
Reg_Input_Data_Placenta_male_ITU_EAAR_wa <- Data_Placenta_male_ITU[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_Years", "smoking_dichotom",  "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders", "maternal_alcohol_use")]

# without alcohol
Reg_Input_Data_Placenta_male_ITU_EAAR_n <- Data_Placenta_male_ITU[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_Years", "smoking_dichotom",  "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders")]


# with alcohol
Reg_Input_Data_Placenta_female_ITU_EAAR_wa <- Data_Placenta_female_ITU[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_Years", "smoking_dichotom",  "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders", "maternal_alcohol_use")]

# without alcohol
Reg_Input_Data_Placenta_female_ITU_EAAR_n <- Data_Placenta_female_ITU[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_Years", "smoking_dichotom",  "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders")]
sapply(Data_Placenta_ITU, function(x) sum(is.na(x)))
                                                            Sample_Name 
                                                                      0 
                                                           Trophoblasts 
                                                                      0 
                                                                Stromal 
                                                                      0 
                                                               Hofbauer 
                                                                      0 
                                                            Endothelial 
                                                                      0 
                                                                   nRBC 
                                                                      0 
                                                    Syncytiotrophoblast 
                                                                      0 
                                                          caseVScontrol 
                                                                      0 
                                                               Warnings 
                                                                      0 
                                                     Maternal_Age_Years 
                                                                      0 
                                                                 Parity 
                                                                      0 
                                                      Mother_Cohabiting 
                                                                     64 
                                        Maternal_Hypertensive_Disorders 
                                                                      0 
                                            Maternal_Diabetes_Disorders 
                                                                      0 
                                              Maternal_Mental_Disorders 
                                                                      0 
                                      Maternal_Smoking_During_Pregnancy 
                                                                      0 
                     Maternal_Corticosteroid_Treatment_during_Pregnancy 
                                                                      0 
                                          Betamethasone_Number_of_Doses 
                                                                      1 
                     Gestational_Weeks_at_First_Betamethasone_Treatment 
                                                                    475 
                      Gestational_Weeks_at_Last_Betamethasone_Treatment 
                                                                    475 
                                     Maternal_Weight_In_Early_Pregnancy 
                                                                      0 
                                                        Maternal_Height 
                                                                      0 
                                                 Maternal_Height_Meters 
                                                                      0 
                            Maternal_Body_Mass_Index_in_Early_Pregnancy 
                                                                      0 
                Maternal_Body_Mass_Index_in_Early_Pregnancy_4categories 
                                                                      0 
                            Weeks_of_Gestation_at_First_Antenatal_Visit 
                                                                      2 
                                       Maternal_Weight_End_of_Pregnancy 
                                                                      2 
                 Gestational_Weeks_At_EndOfPregnancy_Weight_Measurement 
                                                                     11 
                                                       Child_Birth_Year 
                                                                      0 
                                                              Child_Sex 
                                                                      0 
                                                  Gestational_Age_Weeks 
                                                                      0 
                                                   Gestational_Age_Days 
                                                                      0 
                                                     Child_Birth_Weight 
                                                                      0 
                                                     Child_Birth_Length 
                                                                      5 
                                      Child_Head_Circumference_At_Birth 
                                                                     11 
                                                 Placental_Weight_Grams 
                                                                     11 
                                                 Child_Born_DeadorAlive 
                                                                      0 
                                                         Induced_Labour 
                                                                      0 
                                              Child_Apgar_Score_1Minute 
                                                                      1 
                                             Child_Apgar_Score_5Minutes 
                                                                    162 
                                                    Child_NeonatalDeath 
                                                                    486 
                                                         SingletonBirth 
                                                                      0 
                                                         NICU_Treatment 
                                                                      0 
                                                               Asphyxia 
                                                                      0 
                                                      Caesarian_Section 
                                                                      0 
                                                          Delivery_mode 
                                                                      0 
                                                 Delivery_mode_dichotom 
                                                                      0 
                                                        Parity_dichotom 
                                                                      0 
                                                       smoking_dichotom 
                                                                      0 
                                             Maternal_Diabetes_dichotom 
                                                                      0 
                                          Maternal_Hypertension_3levels 
                                                                      0 
                                         Maternal_Hypertension_dichotom 
                                                                      0 
                                                    gestage_at_CVS_days 
                                                                    397 
                                                   gestage_at_CVS_weeks 
                                                                    397 
                                                                preterm 
                                                                      0 
                                                   maternal_alcohol_use 
                                                                     17 
                                  TimeDifferencePlacenta_birth_sampling 
                                                                     54 
                                                              education 
                                                                     19 
                                              education_with_imputation 
                                                                     15 
                                                     maternal_education 
                                                                     19 
                                                        t1_gestageweeks 
                                                                    135 
                                                        t2_gestageweeks 
                                                                    121 
                                                        t3_gestageweeks 
                                                                    110 
                                                             Cesd_trim1 
                                                                    140 
                                                             Cesd_trim2 
                                                                    121 
                                                             Cesd_trim3 
                                                                    111 
                                                   state_anxtotal_trim1 
                                                                    140 
                                                   state_anxtotal_trim2 
                                                                    123 
                                                   state_anxtotal_trim3 
                                                                    111 
                                                              mean_cesd 
                                                                     76 
                                                              mean_stai 
                                                                     77 
                                                          PC1_ethnicity 
                                                                     47 
                                                          PC2_ethnicity 
                                                                     47 
                                                          PC3_ethnicity 
                                                                     47 
                                      ASQ_agespecificquestionnairegroup 
                                                                     94 
                                   ChildAge_ASQ_months_final_30pr_range 
                                                                     99 
         Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange 
                                                                    100 
         Child_ASQ_problemsolving_development_infancy_sum_finalagerange 
                                                                    101 
              Child_ASQ_finemotor_development_infancy_sum_finalagerange 
                                                                     99 
             Child_ASQ_grossmotor_development_infancy_sum_finalagerange 
                                                                     99 
              Child_ASQ_communication_develop_infancy_sum_finalagerange 
                                                                     99 
       Child_ASQ_totaldevelopmentalmilestones_Infancy_Sum_finalagerange 
                                                                    101 
  Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_scaled 
                                                                    100 
  Child_ASQ_problemsolving_development_infancy_sum_finalagerange_scaled 
                                                                    101 
       Child_ASQ_finemotor_development_infancy_sum_finalagerange_scaled 
                                                                     99 
      Child_ASQ_grossmotor_development_infancy_sum_finalagerange_scaled 
                                                                     99 
       Child_ASQ_communication_develop_infancy_sum_finalagerange_scaled 
                                                                     99 
Child_ASQ_totaldevelopmentalmilestones_Infancy_Sum_finalagerange_scaled 
                                                                    101 
     Child_ASQpersonalandsocialskills_dev_infancy_sum_finalagerange_cat 
                                                                    100 
     Child_ASQ_problemsolving_development_infancy_sum_finalagerange_cat 
                                                                    101 
          Child_ASQ_finemotor_development_infancy_sum_finalagerange_cat 
                                                                     99 
         Child_ASQ_grossmotor_development_infancy_sum_finalagerange_cat 
                                                                     99 
          Child_ASQ_communication_develop_infancy_sum_finalagerange_cat 
                                                                     99 
   Child_ASQ_totaldevelopmentalmilestones_Infancy_Sum_finalagerange_cat 
                                                                    101 
                                                          delayed_count 
                                                                    101 
                                                             DNAmGA_Lee 
                                                                      0 
                                                           DNAmGA_Mayne 
                                                                      0 
                                                               EAAR_Lee 
                                                                     47 
                                                             EAAR_Mayne 
                                                                     47 
                                                              delta_Lee 
                                                                      0 
                                                            delta_Mayne 
                                                                      0 
                                                             zdelta_Lee 
                                                                      0 
                                                           zdelta_Mayne 
                                                                      0 

data frame without missings

Reg_Input_Data_Placenta_ITU_EAAR_noNa_n <- na.omit(Reg_Input_Data_Placenta_ITU_EAAR_n) 
dim(Reg_Input_Data_Placenta_ITU_EAAR_noNa_n)
[1] 427  14
Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa <- na.omit(Reg_Input_Data_Placenta_ITU_EAAR_wa) 
dim(Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa)
[1] 412  15
# for split by sex
Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_wa <- na.omit(Reg_Input_Data_Placenta_male_ITU_EAAR_wa) 
dim(Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_wa)
[1] 210  15
Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_n <- na.omit(Reg_Input_Data_Placenta_male_ITU_EAAR_n) 
dim(Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_n)
[1] 218  14
Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_wa <- na.omit(Reg_Input_Data_Placenta_female_ITU_EAAR_wa) 
dim(Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_wa)
[1] 202  15
Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_n <- na.omit(Reg_Input_Data_Placenta_female_ITU_EAAR_n) 
dim(Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_n)
[1] 209  14
skimr::skim(Reg_Input_Data_Placenta_ITU_EAAR_noNa_n)
── Data Summary ────────────────────────
                           Values                      
Name                       Reg_Input_Data_Placenta_I...
Number of rows             427                         
Number of columns          14                          
_______________________                                
Column type frequency:                                 
  factor                   8                           
  numeric                  6                           
________________________                               
Group variables            None                        

── Variable type: factor ────────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable                  n_missing complete_rate ordered n_unique top_counts        
1 Child_Sex                              0             1 FALSE          2 mal: 218, fem: 209
2 smoking_dichotom                       0             1 FALSE          2 no: 411, yes: 16  
3 Delivery_mode_dichotom                 0             1 FALSE          2 una: 305, aid: 122
4 Parity_dichotom                        0             1 FALSE          2 no : 217, giv: 210
5 Induced_Labour                         0             1 FALSE          2 no: 319, yes: 108 
6 Maternal_Hypertension_dichotom         0             1 FALSE          2 no : 402, hyp: 25 
7 Maternal_Diabetes_dichotom             0             1 FALSE          2 no : 333, dia: 94 
8 Maternal_Mental_Disorders              0             1 FALSE          2 No: 379, Yes: 48  

── Variable type: numeric ───────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable                               n_missing complete_rate        mean     sd     p0      p25       p50      p75
1 EAAR_Lee                                            0             1   -0.000685   1.11  -3.72   -0.668    0.0760    0.696
2 Maternal_Age_Years                                  0             1   34.6        4.73  20.3    31.4     34.5      38.0  
3 Maternal_Body_Mass_Index_in_Early_Pregnancy         0             1   23.7        4.02  15.8    21.2     22.7      25.4  
4 Child_Birth_Weight                                  0             1 3542.       507.   805    3265     3580      3868    
5 Child_Birth_Length                                  0             1   50.2        2.42  32      49       50        51    
6 Child_Head_Circumference_At_Birth                   0             1   35.1        1.65  23.5    34       35        36    
     p100 hist 
1    3.56 ▁▂▇▃▁
2   45.5  ▁▃▇▆▂
3   51.0  ▇▆▁▁▁
4 4660    ▁▁▂▇▃
5   57    ▁▁▁▇▁
6   40    ▁▁▁▇▁
save(Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa.Rdata")

save(Reg_Input_Data_Placenta_ITU_EAAR_noNa_n, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_ITU_EAAR_noNa_n.Rdata")
save(Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_wa, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_wa.Rdata")
save(Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_n, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_n.Rdata")

save(Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_wa, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_wa.Rdata")
save(Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_n, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_n.Rdata")

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cord blood data preparation for model

EPIC

regression input

# EAAR without alcohol
Reg_Input_Data_Cordblood_PREDO_EAAR_n <- Data_PREDO_EPICcord[, c("EAAR_Bohlin", "Child_Sex", "Gestational_Age", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]

# EAAR with alcohol
Reg_Input_Data_Cordblood_PREDO_EAAR_wa <- Data_PREDO_EPICcord[, c("EAAR_Bohlin", "Child_Sex", "Gestational_Age", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Alcohol_Use_In_Early_Pregnancy_19Oct", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]

data frame without missings

Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n <- na.omit(Reg_Input_Data_Cordblood_PREDO_EAAR_n) 
dim(Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n)
[1] 144  15
Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_wa <- na.omit(Reg_Input_Data_Cordblood_PREDO_EAAR_wa) 
dim(Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_wa)
[1] 130  16
skimr::skim(Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n)
── Data Summary ────────────────────────
                           Values                      
Name                       Reg_Input_Data_Cordblood_...
Number of rows             144                         
Number of columns          15                          
_______________________                                
Column type frequency:                                 
  factor                   8                           
  numeric                  7                           
________________________                               
Group variables            None                        

── Variable type: factor ────────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable                           n_missing complete_rate ordered n_unique top_counts       
1 Child_Sex                                       0             1 FALSE          2 fem: 73, mal: 71 
2 smoking_dichotom                                0             1 FALSE          2 no: 131, yes: 13 
3 Delivery_Mode_dichotom                          0             1 FALSE          2 una: 93, aid: 51 
4 Parity_dichotom                                 0             1 FALSE          2 giv: 81, no : 63 
5 inducedlabour                                   0             1 FALSE          2 No: 108, Yes: 36 
6 maternal_diabetes_dichotom                      0             1 FALSE          2 no : 119, dia: 25
7 maternal_hypertension_dichotom                  0             1 FALSE          2 no : 108, hyp: 36
8 Maternal_Mental_Disorders_By_Childbirth         0             1 FALSE          2 No: 126, Yes: 18 

── Variable type: numeric ───────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable                      n_missing complete_rate       mean      sd      p0      p25       p50      p75    p100
1 EAAR_Bohlin                                0             1   -0.00328   0.464   -1.10   -0.354    0.0201    0.310    1.08
2 Gestational_Age                            0             1   39.8       1.44    32.4    39.1     39.9      40.9     42.3 
3 Maternal_Age_18PopRegandBR                 0             1   32.1       4.98    19.4    28.2     32.1      35.4     43.4 
4 Maternal_PrepregnancyBMI18oct28new         0             1   25.2       5.76    17.2    21.2     23.4      27.4     46.5 
5 Birth_Weight                               0             1 3443.      518.    1100    3138.    3505      3771.    4810   
6 Birth_Length                               0             1   49.7       2.46    35      49       50        51       55   
7 Head_Circumference_at_Birth                0             1   35.2       1.35    31      34       35        36       38.5 
  hist 
1 ▂▆▇▅▂
2 ▁▁▂▇▆
3 ▁▅▇▆▂
4 ▇▆▂▁▁
5 ▁▁▅▇▁
6 ▁▁▂▇▂
7 ▁▅▇▆▁
save(Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_wa, file="InputData/ClockCalculationsInput/Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_wa.Rdata")

save(Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n, file="InputData/ClockCalculationsInput/Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n.Rdata")

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cord blood data preparation for model

450K

regression input

# EAAR without alcohol
Reg_Input_Data_Cordblood_PREDO450K_EAAR_n <- Data_PREDO_450Kcord[, c("EAAR_Bohlin", "Child_Sex", "Gestational_Age", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]

#EAAR with alcohol
Reg_Input_Data_Cordblood_PREDO450K_EAAR_wa <- Data_PREDO_450Kcord[, c("EAAR_Bohlin", "Child_Sex", "Gestational_Age", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Alcohol_Use_In_Early_Pregnancy_19Oct", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]
sapply(Reg_Input_Data_Cordblood_PREDO450K_EAAR_wa, function(x) sum(is.na(x)))
                            EAAR_Bohlin                               Child_Sex                         Gestational_Age 
                                     10                                       0                                       2 
             Maternal_Age_18PopRegandBR                        smoking_dichotom    Alcohol_Use_In_Early_Pregnancy_19Oct 
                                      0                                       0                                     102 
                 Delivery_Mode_dichotom      Maternal_PrepregnancyBMI18oct28new                            Birth_Weight 
                                     19                                       0                                       3 
                           Birth_Length             Head_Circumference_at_Birth                         Parity_dichotom 
                                      3                                       3                                       6 
                          inducedlabour              maternal_diabetes_dichotom          maternal_hypertension_dichotom 
                                      3                                       0                                       0 
Maternal_Mental_Disorders_By_Childbirth 
                                      1 

data frame without missings

Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_wa <- na.omit(Reg_Input_Data_Cordblood_PREDO450K_EAAR_wa) 
dim(Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_wa)
[1] 665  16
Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n <- na.omit(Reg_Input_Data_Cordblood_PREDO450K_EAAR_n) 
dim(Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n)
[1] 766  15
skimr::skim(Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n)
── Data Summary ────────────────────────
                           Values                      
Name                       Reg_Input_Data_Cordblood_...
Number of rows             144                         
Number of columns          15                          
_______________________                                
Column type frequency:                                 
  factor                   8                           
  numeric                  7                           
________________________                               
Group variables            None                        

── Variable type: factor ────────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable                           n_missing complete_rate ordered n_unique top_counts       
1 Child_Sex                                       0             1 FALSE          2 fem: 73, mal: 71 
2 smoking_dichotom                                0             1 FALSE          2 no: 131, yes: 13 
3 Delivery_Mode_dichotom                          0             1 FALSE          2 una: 93, aid: 51 
4 Parity_dichotom                                 0             1 FALSE          2 giv: 81, no : 63 
5 inducedlabour                                   0             1 FALSE          2 No: 108, Yes: 36 
6 maternal_diabetes_dichotom                      0             1 FALSE          2 no : 119, dia: 25
7 maternal_hypertension_dichotom                  0             1 FALSE          2 no : 108, hyp: 36
8 Maternal_Mental_Disorders_By_Childbirth         0             1 FALSE          2 No: 126, Yes: 18 

── Variable type: numeric ───────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable                      n_missing complete_rate       mean      sd      p0      p25       p50      p75    p100
1 EAAR_Bohlin                                0             1   -0.00328   0.464   -1.10   -0.354    0.0201    0.310    1.08
2 Gestational_Age                            0             1   39.8       1.44    32.4    39.1     39.9      40.9     42.3 
3 Maternal_Age_18PopRegandBR                 0             1   32.1       4.98    19.4    28.2     32.1      35.4     43.4 
4 Maternal_PrepregnancyBMI18oct28new         0             1   25.2       5.76    17.2    21.2     23.4      27.4     46.5 
5 Birth_Weight                               0             1 3443.      518.    1100    3138.    3505      3771.    4810   
6 Birth_Length                               0             1   49.7       2.46    35      49       50        51       55   
7 Head_Circumference_at_Birth                0             1   35.2       1.35    31      34       35        36       38.5 
  hist 
1 ▂▆▇▅▂
2 ▁▁▂▇▆
3 ▁▅▇▆▂
4 ▇▆▂▁▁
5 ▁▁▅▇▁
6 ▁▁▂▇▂
7 ▁▅▇▆▁
save(Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_wa, file="InputData/ClockCalculationsInput/Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_wa.Rdata")

save(Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n, file="InputData/ClockCalculationsInput/Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n.Rdata")

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placenta: data preparation for model

Placenta EPIC

regression input

# EAAR (with ethnicity) without alcohol
Reg_Input_Data_Placenta_PREDO_EAAR_n <- Data_PREDO_EPICplacenta[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]

# EAAR (with ethnicity) with alcohol
Reg_Input_Data_Placenta_PREDO_EAAR_wa <- Data_PREDO_EPICplacenta[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Alcohol_Use_In_Early_Pregnancy_19Oct", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]
# for split by sex
# with alcohol
Reg_Input_Data_Placenta_male_PREDO_EAAR_wa <- Data_PREDO_Placenta_male[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Alcohol_Use_In_Early_Pregnancy_19Oct", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]

# without alcohol
Reg_Input_Data_Placenta_male_PREDO_EAAR_n <- Data_PREDO_Placenta_male[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]


# with alcohol
Reg_Input_Data_Placenta_female_PREDO_EAAR_wa <- Data_PREDO_Placenta_female[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Alcohol_Use_In_Early_Pregnancy_19Oct", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]

# without alcohol
Reg_Input_Data_Placenta_female_PREDO_EAAR_n <- Data_PREDO_Placenta_female[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]

data frame without missings

Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n <- na.omit(Reg_Input_Data_Placenta_PREDO_EAAR_n) 
dim(Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n)
[1] 117  14
Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa <- na.omit(Reg_Input_Data_Placenta_PREDO_EAAR_wa) 
dim(Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa)
[1] 106  15
Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_n <- na.omit(Reg_Input_Data_Placenta_male_PREDO_EAAR_n) 
dim(Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_n)
[1] 56 14
Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_wa <- na.omit(Reg_Input_Data_Placenta_male_PREDO_EAAR_wa) 
dim(Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_wa)
[1] 52 15
Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_n <- na.omit(Reg_Input_Data_Placenta_female_PREDO_EAAR_n) 
dim(Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_n)
[1] 61 14
Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_wa <- na.omit(Reg_Input_Data_Placenta_female_PREDO_EAAR_wa) 
dim(Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_wa)
[1] 54 15
skimr::skim(Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n)
── Data Summary ────────────────────────
                           Values                      
Name                       Reg_Input_Data_Placenta_P...
Number of rows             117                         
Number of columns          14                          
_______________________                                
Column type frequency:                                 
  factor                   8                           
  numeric                  6                           
________________________                               
Group variables            None                        

── Variable type: factor ────────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable                           n_missing complete_rate ordered n_unique top_counts      
1 Child_Sex                                       0             1 FALSE          2 fem: 61, mal: 56
2 smoking_dichotom                                0             1 FALSE          2 no: 107, yes: 10
3 Delivery_Mode_dichotom                          0             1 FALSE          2 una: 73, aid: 44
4 Parity_dichotom                                 0             1 FALSE          2 giv: 65, no : 52
5 inducedlabour                                   0             1 FALSE          2 No: 93, Yes: 24 
6 maternal_diabetes_dichotom                      0             1 FALSE          2 no : 98, dia: 19
7 maternal_hypertension_dichotom                  0             1 FALSE          2 no : 90, hyp: 27
8 Maternal_Mental_Disorders_By_Childbirth         0             1 FALSE          2 No: 103, Yes: 14

── Variable type: numeric ───────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable                      n_missing complete_rate      mean      sd      p0      p25       p50      p75    p100
1 EAAR_Lee                                   0             1   -0.0148   0.894   -2.86   -0.467    0.0580    0.597    2.39
2 Maternal_Age_18PopRegandBR                 0             1   32.1      4.67    22.3    28.7     31.8      35.3     43.4 
3 Maternal_PrepregnancyBMI18oct28new         0             1   25.0      5.74    17.7    21.0     23.4      26.6     46.5 
4 Birth_Weight                               0             1 3453.     533.    1100    3140     3525      3800     4810   
5 Birth_Length                               0             1   49.7      2.57    35      49       50        51       55   
6 Head_Circumference_at_Birth                0             1   35.2      1.38    31      34       35        36       38.5 
  hist 
1 ▁▃▇▆▁
2 ▃▆▇▅▂
3 ▇▆▁▁▁
4 ▁▁▅▇▂
5 ▁▁▂▇▂
6 ▁▅▇▇▁
save(Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa.Rdata")
save(Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n.Rdata")
save(Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_wa, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_wa.Rdata")
save(Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_n, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_n.Rdata")

save(Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_wa, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_wa.Rdata")
save(Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_n, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_n.Rdata")

to the top


Sample visualization

Fig. 1

Venn_ITU <- euler(c("CVS"=264, "Placenta \n(fetal side)"=486, "Cord blood"=426, "CVS&Placenta \n(fetal side)"=86, "Placenta \n(fetal side)&Cord blood"=390, "CVS&Cord blood"=73, "CVS&Placenta \n(fetal side)&Cord blood"=66))

Venn_PREDO <- euler(c("Placenta \n(decidual \nside)"=139, "Cord \nblood \n(EPIC)"=149, "Cord blood (450K)"=795, "Placenta \n(decidual \nside)&Cord \nblood \n(EPIC)"=117))

plot(Venn_ITU, counts=TRUE, font=1, cex=2, alpha=0.5, fill=c("grey", "lightgrey", "darkgrey"), labels=F)
grid::grid.text("CVS \nn = 264", x=0.3, y=0.3, gp=gpar(col="black", fontsize=11, font="Arial")) #CVS
grid::grid.text("Placenta \n(fetal side)\nn = 486", x=0.6, y=0.2, gp=gpar(col="black", fontsize=11, font="Arial")) #placenta
grid::grid.text("Cord blood\nn = 426", x=0.5, y=0.8, gp=gpar(col="black", fontsize=11, font="Arial")) #cord
grid::grid.text("73", x=0.35, y=0.55, gp=gpar(col="black", fontsize=10, font="Arial")) #cvs cord
grid::grid.text("86", x=0.43, y=0.26, gp=gpar(col="black", fontsize=10, font="Arial")) #cvs placenta
grid::grid.text("390", x=0.6, y=0.5, gp=gpar(col="black", fontsize=10, font="Arial")) #cord placenta
grid::grid.text("66", x=0.43, y=0.45, gp=gpar(col="black", fontsize=10, font="Arial")) #all

plot(Venn_PREDO, counts=TRUE, font=1, cex=1, alpha=0.5, fill=c("grey", "lightgrey", "darkgrey"), labels=F)
grid::grid.text("Placenta\n(decidual side) \nn = 139", x=0.08, y=0.3, gp=gpar(col="black", fontsize=11, font="Arial")) # placenta
grid::grid.text("Cord blood\n(EPIC) \nn = 149", x=0.37, y=0.3, gp=gpar(col="black", fontsize=11, font="Arial")) # cord epic
grid::grid.text("Cord blood\n(450K) \nn = 795", x=0.72, y=0.5, gp=gpar(col="black", fontsize=11, font="Arial")) # cord 450k
grid::grid.text("117", x=0.23, y=0.3, gp=gpar(col="black", fontsize=10, font="Arial")) # overlap
```r
ifelse(!dir.exists(file.path(getwd(), \Results/\)), dir.create(file.path(getwd(), \Results/\)), FALSE)

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```r
```r
ifelse(!dir.exists(file.path(getwd(), \Results/Figures/\)), dir.create(file.path(getwd(), \Results/Figures/\)), FALSE)

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```r
png(filename="Results/Figures/ITU_sample.png", width=2300, height=1500, res=300)
plot(Venn_ITU, counts=TRUE, font=1, cex=2, alpha=0.5, fill=c("grey", "lightgrey", "darkgrey"), labels=F)
grid::grid.text("CVS \nn = 264", x=0.3, y=0.3, gp=gpar(col="black", fontsize=11, font="Arial")) #CVS
grid::grid.text("Placenta \n(fetal side)\nn = 486", x=0.6, y=0.2, gp=gpar(col="black", fontsize=11, font="Arial")) #placenta
grid::grid.text("Cord blood\nn = 426", x=0.5, y=0.8, gp=gpar(col="black", fontsize=11, font="Arial")) #cord
grid::grid.text("73", x=0.35, y=0.55, gp=gpar(col="black", fontsize=10, font="Arial")) #cvs cord
grid::grid.text("86", x=0.43, y=0.26, gp=gpar(col="black", fontsize=10, font="Arial")) #cvs placenta
grid::grid.text("390", x=0.6, y=0.5, gp=gpar(col="black", fontsize=10, font="Arial")) #cord placenta
grid::grid.text("66", x=0.43, y=0.45, gp=gpar(col="black", fontsize=10, font="Arial")) #all
dev.off()
png(filename="Results/Figures/PREDO_sample.png", width=2300, height=1500, res=300)
plot(Venn_PREDO, counts=TRUE, font=1, cex=1, alpha=0.5, fill=c("grey", "lightgrey", "darkgrey"), labels=F)
grid::grid.text("Placenta\n(decidual side) \nn = 139", x=0.08, y=0.3, gp=gpar(col="black", fontsize=11, font="Arial")) # placenta
grid::grid.text("Cord blood\n(EPIC) \nn = 149", x=0.37, y=0.3, gp=gpar(col="black", fontsize=11, font="Arial")) # cord epic
grid::grid.text("Cord blood\n(450K) \nn = 795", x=0.72, y=0.5, gp=gpar(col="black", fontsize=11, font="Arial")) # cord 450k
grid::grid.text("117", x=0.23, y=0.3, gp=gpar(col="black", fontsize=10, font="Arial")) # overlap
dev.off()

ITU Descriptives

Table 1 & 2

ifelse(!dir.exists(file.path(getwd(), "Results/Figures/diffTissues")), dir.create(file.path(getwd(), "Results/Figures/diffTissues")), FALSE)

ITU CVS

Clock

knitr::kable(
  psych::describe(Data_CVS_ITU[ ,c("Gestational_Age_Weeks", "gestage_at_CVS_weeks","DNAmGA_Lee","delta_Lee","zdelta_Lee", "EAAR_Lee", "DNAmGA_Mayne","delta_Mayne","zdelta_Mayne","EAAR_Mayne")])
)

Cell types

knitr::kable(
  psych::describe(Data_CVS_ITU[ ,c("Trophoblasts", "Stromal", "Hofbauer", "Endothelial", "nRBC", "Syncytiotrophoblast")])
)

Data_cells_cvs_itu <- Data_CVS_ITU[ ,c("Trophoblasts", "Stromal", "Hofbauer", "Endothelial", "nRBC", "Syncytiotrophoblast")]

cells_cvs <- data.frame(psych::describe(Data_CVS_ITU[ ,c("Trophoblasts", "Stromal", "Hofbauer", "Endothelial", "nRBC", "Syncytiotrophoblast")]))
cells_cvs_ <- cells_cvs[ ,c("mean", "sd")]

plot_cells_cvs <- ggplot(cells_cvs, aes(x=as.factor(rownames(cells_cvs)), y=mean)) +
  geom_bar(position=position_dodge(), stat="identity", colour='black') +
  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,position=position_dodge(.9))+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(x ="\nCVS (ITU)")

png(filename="Results/Figures/diffTissues/cvs_cells_itu.png", width=2300, height=1500, res=400)
plot_cells_cvs
dev.off()

predictors descriptive

```r
CVS_Preds_ITU <- Data_CVS_ITU[,c(\Child_Sex\, \Delivery_mode_dichotom\, \Induced_Labour\, \Parity_dichotom\, \Maternal_Hypertension_dichotom\, \Maternal_Diabetes_dichotom\, \Maternal_Mental_Disorders\, \smoking_dichotom\, \maternal_alcohol_use\, \Maternal_Age_Years\, \Maternal_Body_Mass_Index_in_Early_Pregnancy\, \Child_Birth_Weight\, \Child_Birth_Length\, \Child_Head_Circumference_At_Birth\)]
colnames(CVS_Preds_ITU) <- c(\child_sex\, \delivery_mode\, \induced_labor\, \parity\, \hypertension\, \diabetes\, \mental_disorders\, \smoking\, \alcohol\, \maternal_age\, \maternal_BMI\, \birth_weight\, \birth_length\, \head_circumference\)
CVS_Preds_ITU$group <- \ITU\

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```r
CVS_Preds_ITU %>%  
select_if(is.factor) %>% 
Hmisc::describe()
CVS_Preds_ITU %>%
select_if(is.numeric) %>% 
psych::describe()
  • model without alcohol
Reg_Input_Data_CVS_ITU_EAAR_n_noNa %>%
  select_if(is.factor) %>%
  Hmisc::describe()

Reg_Input_Data_CVS_ITU_EAAR_n_noNa %>%
  select_if(is.numeric) %>%
  Hmisc::describe()
  • model with alcohol
Reg_Input_Data_CVS_ITU_EAAR_wa_noNa %>%
  select_if(is.factor) %>%
  Hmisc::describe()

#alcohol use 14.3%
Reg_Input_Data_CVS_ITU_EAAR_wa_noNa %>%
  select_if(is.numeric) %>%
  Hmisc::describe()

ITU Cord blood

Clocks

knitr::kable(
psych::describe(Data_Cord_ITU[ ,c("Gestational_Age_Weeks","DNAmGA_Knight","delta_Knight","zdelta_Knight", "EAAR_Knight", "DNAmGA_Bohlin","delta_Bohlin","zdelta_Bohlin", "EAAR_Bohlin")])
)

cell types

knitr::kable(
  psych::describe(Data_Cord_ITU[ ,c("CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", "nRBC")])
)

Data_cells_cord <- Data_Cord_ITU[ ,c("Sample_Name", "CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", "nRBC")]

cells_cord <- data.frame(psych::describe(Data_Cord_ITU[ ,c("CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", "nRBC")]))
cells_cord <- cells_cord[ ,c("mean", "sd")]
rownames(cells_cord) <- c("CD8T", "CD46", "NK", "Bcell", "Monocytes", "Granulocytes", "nRBC")

plot_cells_cord <- ggplot(cells_cord, aes(x=as.factor(rownames(cells_cord)), y=mean)) +
  geom_bar(position=position_dodge(), stat="identity", colour='black') +
  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,position=position_dodge(.9))+
  labs(x ="\nCord blood (ITU)")+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

png(filename="Results/Figures/diffTissues/cord_cells_itu.png", width=2300, height=1500, res=400)
plot_cells_cord
dev.off()

predictors descriptive

```r
Cordblood_Preds_ITU <- Data_Cord_ITU[,c(\Child_Sex\, \Delivery_mode_dichotom\, \Induced_Labour\, \Parity_dichotom\, \Maternal_Hypertension_dichotom\, \Maternal_Diabetes_dichotom\, \Maternal_Mental_Disorders\, \smoking_dichotom\, \maternal_alcohol_use\, \Maternal_Age_Years\, \Maternal_Body_Mass_Index_in_Early_Pregnancy\, \Child_Birth_Weight\, \Child_Birth_Length\, \Child_Head_Circumference_At_Birth\)]
colnames(Cordblood_Preds_ITU) <- c(\child_sex\, \delivery_mode\, \induced_labor\, \parity\, \hypertension\, \diabetes\, \mental_disorders\, \smoking\, \alcohol\, \maternal_age\, \maternal_BMI\, \birth_weight\, \birth_length\, \head_circumference\)
Cordblood_Preds_ITU$group <- \ITU\

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```r
Cordblood_Preds_ITU %>%  
select_if(is.factor) %>% 
Hmisc::describe()
Cordblood_Preds_ITU %>%  
select_if(is.numeric) %>% 
Hmisc::describe()
  • model without alcohol
Reg_Input_Data_Cord_ITU_EAAR_noNa_n %>%
  select_if(is.factor) %>%
  Hmisc::describe()

Reg_Input_Data_Cord_ITU_EAAR_noNa_n %>%
  select_if(is.numeric) %>%
  Hmisc::describe()
  • model with alcohol
Reg_Input_Data_Cord_ITU_EAAR_noNa_wa %>%
  select_if(is.factor) %>%
  Hmisc::describe()

Reg_Input_Data_Cord_ITU_EAAR_noNa_wa %>%
  select_if(is.numeric) %>%
  Hmisc::describe()

#10.4% maternal alcohol use

ITU Placenta

Clocks

knitr::kable(
psych::describe(Data_Placenta_ITU[ ,c("Gestational_Age_Weeks","DNAmGA_Lee","delta_Lee","zdelta_Lee", "EAAR_Lee", "DNAmGA_Mayne","delta_Mayne","zdelta_Mayne","EAAR_Mayne", "TimeDifferencePlacenta_birth_sampling")])
)

cell types

knitr::kable(
  psych::describe(Data_Placenta_ITU[ ,c("Trophoblasts", "Stromal", "Hofbauer", "Endothelial", "nRBC", "Syncytiotrophoblast")])
)

Data_cells_placenta_itu <- Data_Placenta_ITU[ ,c("Trophoblasts", "Stromal", "Hofbauer", "Endothelial", "nRBC", "Syncytiotrophoblast")]

cells_placenta <- data.frame(psych::describe(Data_Placenta_ITU[ ,c("Trophoblasts", "Stromal", "Hofbauer", "Endothelial", "nRBC", "Syncytiotrophoblast")]))
cells_placenta <- cells_placenta[ ,c("mean", "sd")]

plot_cells_placenta <- ggplot(cells_placenta, aes(x=as.factor(rownames(cells_placenta)), y=mean)) +
  geom_bar(position=position_dodge(), stat="identity", colour='black') +
  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,position=position_dodge(.9))+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(x ="\nfetal Placenta (ITU)")

png(filename="Results/Figures/diffTissues/placenta_cells_itu.png", width=2300, height=1500, res=400)
plot_cells_placenta
dev.off()
plot_cells_placenta

predictors descriptive

```r
Placenta_Preds_ITU <- Data_Placenta_ITU[,c(\Child_Sex\, \Delivery_mode_dichotom\, \Induced_Labour\, \Parity_dichotom\, \Maternal_Hypertension_dichotom\, \Maternal_Diabetes_dichotom\, \Maternal_Mental_Disorders\, \smoking_dichotom\, \maternal_alcohol_use\, \Maternal_Age_Years\, \Maternal_Body_Mass_Index_in_Early_Pregnancy\, \Child_Birth_Weight\, \Child_Birth_Length\, \Child_Head_Circumference_At_Birth\)]
colnames(Placenta_Preds_ITU) <- c(\child_sex\, \delivery_mode\, \induced_labor\, \parity\, \hypertension\, \diabetes\, \mental_disorders\, \smoking\, \alcohol\, \maternal_age\, \maternal_BMI\, \birth_weight\, \birth_length\, \head_circumference\)
Placenta_Preds_ITU$group <- \ITU\

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```r
Placenta_Preds_ITU %>%  
select_if(is.factor) %>% 
Hmisc::describe()
Placenta_Preds_ITU %>%  
select_if(is.numeric) %>% 
Hmisc::describe()
  • model without alcohol
Reg_Input_Data_Placenta_ITU_EAAR_noNa_n %>%
  select_if(is.factor) %>%
  Hmisc::describe()

Reg_Input_Data_Placenta_ITU_EAAR_noNa_n %>%
  select_if(is.numeric) %>%
  Hmisc::describe()
  • model with alcohol
Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa %>%
  select_if(is.factor) %>%
  Hmisc::describe()

Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa %>%
  select_if(is.numeric) %>%
  Hmisc::describe()

# alcohol use 10.2%

to the top

PREDO Descriptives

Cord blood EPIC

Clocks

knitr::kable(
  psych::describe(Data_PREDO_EPICcord[,c("Gestational_Age","DNAmGA_Knight","delta_Knight","zdelta_Knight", "EAAR_Knight","DNAmGA_Bohlin","delta_Bohlin","zdelta_Bohlin",  "EAAR_Bohlin")])
)

cell types

knitr::kable(
  psych::describe(Data_PREDO_EPICcord[ ,c("CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", "nRBC")])
)

Data_cells_cord_epic <- Data_PREDO_EPICcord[ ,c("Sample_Name", "CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", "nRBC")]
  

cells_cord_epic <- data.frame(psych::describe(Data_PREDO_EPICcord[ ,c("CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", "nRBC")]))
cells_cord_epic <- cells_cord_epic[ ,c("mean", "sd")]
rownames(cells_cord_epic) <- c("CD8T", "CD4T", "NK", "Bcell", "Monocytes", "Granulocytes", "nRBC")
  

plot_cells_cord_epic <- ggplot(cells_cord_epic, aes(x=as.factor(rownames(cells_cord_epic)), y=mean)) +
  geom_bar(position=position_dodge(), stat="identity", colour='black') +
  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,position=position_dodge(.9))+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(x ="\nCord blood EPIC (PREDO)")

png(filename="Results/Figures/diffTissues/cordepic_cells_predo.png", width=2300, height=1500, res=400)
plot_cells_cord_epic
dev.off()
plot_cells_cord_epic

predictors descriptive

```r
Cordblood_Preds_PREDO <- Data_PREDO_EPICcord[,c(\Child_Sex\,\Delivery_Mode_dichotom\,\inducedlabour\,\Parity_dichotom\, \maternal_hypertension_dichotom\, \maternal_diabetes_dichotom\, \Maternal_Mental_Disorders_By_Childbirth\,\smoking_dichotom\,\Alcohol_Use_In_Early_Pregnancy_19Oct\,\Maternal_Age_18PopRegandBR\,   \Maternal_PrepregnancyBMI18oct28new\, \Birth_Weight\, \Birth_Length\, \Head_Circumference_at_Birth\)]
colnames(Cordblood_Preds_PREDO) <- c(\child_sex\, \delivery_mode\, \induced_labor\, \parity\, \hypertension\, \diabetes\, \mental_disorders\, \smoking\, \alcohol\, \maternal_age\, \maternal_BMI\, \birth_weight\, \birth_length\, \head_circumference\)
Cordblood_Preds_PREDO$group <- \PREDO\
levels(Cordblood_Preds_PREDO$induced_labor)[levels(Cordblood_Preds_PREDO$induced_labor)==\Yes\] <- \yes\
levels(Cordblood_Preds_PREDO$induced_labor)[levels(Cordblood_Preds_PREDO$induced_labor)==\No\] <- \no\
levels(Cordblood_Preds_PREDO$diabetes)[levels(Cordblood_Preds_PREDO$diabetes)==\no diabetes in current pregnancy\] <- \no diabetes this pregnancy\

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```r
Cordblood_Preds_PREDO %>%  
select_if(is.factor) %>% 
Hmisc::describe()
Cordblood_Preds_PREDO %>%
select_if(is.numeric) %>% 
psych::describe()

Cord blood 450K

Clocks

knitr::kable(
  psych::describe(Data_PREDO_450Kcord[ ,c("Gestational_Age","DNAmGA_Knight","delta_Knight","zdelta_Knight", "EAAR_Knight", "DNAmGA_Bohlin","delta_Bohlin","zdelta_Bohlin", "EAAR_Bohlin")])
)

cell types

knitr::kable(
  psych::describe(Data_PREDO_450Kcord[ ,c("CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", "nRBC")])
)

Data_cells_cord_450 <- Data_PREDO_450Kcord[ ,c("Sample_Name", "CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", "nRBC")]
  
cells_cord_450K <- data.frame(psych::describe(Data_PREDO_450Kcord[ ,c("CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", "nRBC")]))
cells_cord_450K <- cells_cord_450K[ ,c("mean", "sd")]
rownames(cells_cord_450K) <- c("CD8T", "CD4T", "NK", "Bcell", "Monocytes", "Granulocytes", "nRBC")
  
plot_cells_cord_450K <- ggplot(cells_cord_450K, aes(x=as.factor(rownames(cells_cord_450K)), y=mean)) +
  geom_bar(position=position_dodge(), stat="identity", colour='black') +
  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,position=position_dodge(.9))+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(x ="\nCord blood 450K (PREDO)")

png(filename="Results/Figures/diffTissues/cord450k_cells_predo.png", width=2300, height=1500, res=400)
plot_cells_cord_450K
dev.off()
plot_cells_cord_450K

predictors descriptive

```r
Cordblood_Preds450K_PREDO <- Data_PREDO_450Kcord[,c(\Child_Sex\,\Delivery_Mode_dichotom\,\inducedlabour\,\Parity_dichotom\, \maternal_hypertension_dichotom\, \maternal_diabetes_dichotom\, \Maternal_Mental_Disorders_By_Childbirth\,\smoking_dichotom\,\Alcohol_Use_In_Early_Pregnancy_19Oct\,\Maternal_Age_18PopRegandBR\,   \Maternal_PrepregnancyBMI18oct28new\, \Birth_Weight\, \Birth_Length\, \Head_Circumference_at_Birth\)]
colnames(Cordblood_Preds450K_PREDO) <- c(\child_sex\, \delivery_mode\, \induced_labor\, \parity\, \hypertension\, \diabetes\, \mental_disorders\, \smoking\, \alcohol\, \maternal_age\, \maternal_BMI\, \birth_weight\, \birth_length\, \head_circumference\)
Cordblood_Preds450K_PREDO$group <- \PREDO\
levels(Cordblood_Preds450K_PREDO$induced_labor)[levels(Cordblood_Preds450K_PREDO$induced_labor)==\Yes\] <- \yes\
levels(Cordblood_Preds450K_PREDO$induced_labor)[levels(Cordblood_Preds450K_PREDO$induced_labor)==\No\] <- \no\
levels(Cordblood_Preds450K_PREDO$diabetes)[levels(Cordblood_Preds450K_PREDO$diabetes)==\no diabetes in current pregnancy\] <- \no diabetes this pregnancy\

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```r
Cordblood_Preds450K_PREDO %>%  
select_if(is.factor) %>% 
Hmisc::describe()
Cordblood_Preds450K_PREDO %>%
select_if(is.numeric) %>% 
psych::describe()

Placenta EPIC

Clocks

knitr::kable(
  psych::describe(Data_PREDO_EPICplacenta[,c("Gestational_Age","DNAmGA_Lee","delta_Lee","zdelta_Lee", "EAAR_Lee", "DNAmGA_Mayne","delta_Mayne","zdelta_Mayne", "EAAR_Mayne")])
)

cell types

knitr::kable(
  psych::describe(Data_PREDO_EPICplacenta[ ,c("Trophoblasts", "Stromal", "Hofbauer", "Endothelial", "nRBC", "Syncytiotrophoblast")])
)

Data_cells_placenta_pred <- Data_PREDO_EPICplacenta[ ,c("Trophoblasts", "Stromal", "Hofbauer", "Endothelial", "nRBC", "Syncytiotrophoblast")]

cells_placenta_predo <- data.frame(psych::describe(Data_PREDO_EPICplacenta[ ,c("Trophoblasts", "Stromal", "Hofbauer", "Endothelial", "nRBC", "Syncytiotrophoblast")]))
cells_cvs <- cells_cvs[ ,c("mean", "sd")]

plot_cells_placenta_predo <- ggplot(cells_placenta_predo, aes(x=as.factor(rownames(cells_placenta_predo)), y=mean)) +
  geom_bar(position=position_dodge(), stat="identity", colour='black') +
  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,position=position_dodge(.9))+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(x ="\ndecidual Placenta (PREDO)")

png(filename="Results/Figures/diffTissues/placenta_cells_predo.png", width=2300, height=1500, res=400)
plot_cells_placenta_predo
dev.off()
plot_cells_placenta_predo

predictors descriptive

```r
Placenta_Preds_PREDO <- Data_PREDO_EPICplacenta[,c(\Child_Sex\,\Delivery_Mode_dichotom\,\inducedlabour\,\Parity_dichotom\, \maternal_hypertension_dichotom\, \maternal_diabetes_dichotom\, \Maternal_Mental_Disorders_By_Childbirth\,\smoking_dichotom\,\Alcohol_Use_In_Early_Pregnancy_19Oct\,\Maternal_Age_18PopRegandBR\,   \Maternal_PrepregnancyBMI18oct28new\, \Birth_Weight\, \Birth_Length\, \Head_Circumference_at_Birth\)]
colnames(Placenta_Preds_PREDO) <- c(\child_sex\, \delivery_mode\, \induced_labor\, \parity\, \hypertension\, \diabetes\, \mental_disorders\, \smoking\, \alcohol\, \maternal_age\, \maternal_BMI\, \birth_weight\, \birth_length\, \head_circumference\)
Placenta_Preds_PREDO$group <- \PREDO\
levels(Placenta_Preds_PREDO$induced_labor)[levels(Placenta_Preds_PREDO$induced_labor)==\Yes\] <- \yes\
levels(Placenta_Preds_PREDO$induced_labor)[levels(Placenta_Preds_PREDO$induced_labor)==\No\] <- \no\
levels(Placenta_Preds_PREDO$diabetes)[levels(Placenta_Preds_PREDO$diabetes)==\no diabetes in current pregnancy\] <- \no diabetes this pregnancy\

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```r
Placenta_Preds_PREDO %>%  
select_if(is.factor) %>% 
Hmisc::describe()
Placenta_Preds_PREDO %>%
select_if(is.numeric) %>% 
psych::describe()
  • model without alcohol
Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n %>%
  select_if(is.factor) %>%
  Hmisc::describe()

Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n %>%
  select_if(is.numeric) %>%
  Hmisc::describe()
  • model with alcohol
Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa %>%
  select_if(is.factor) %>%
  Hmisc::describe()

Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa %>%
  select_if(is.numeric) %>%
  Hmisc::describe()

#12.3% maternal alcohol use

to the top

Cell Type Overview

Cell Type Overview ITU & PREDO

#grid.arrange(plot_cells_cord, plot_cells_cord_epic, plot_cells_cord_450K, ncol=3)

ggarrange(plot_cells_cord +
               theme(axis.ticks.y = element_blank(),
                     plot.margin = margin(r = 1) ), 
          plot_cells_cord_epic + 
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(),
                     axis.title.y = element_blank(),
                     plot.margin = margin(r = 1, l = 1) ), 
          plot_cells_cord_450K + 
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(),
                     axis.title.y = element_blank(),
                     plot.margin = margin(l = 1)  ),
          nrow = 1)

ggarrange(plot_cells_cvs +
               theme(axis.ticks.y = element_blank(),
                     plot.margin = margin(r = 1) ), 
          plot_cells_placenta + 
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(),
                     axis.title.y = element_blank(),
                     plot.margin = margin(r = 1, l = 1) ), 
          plot_cells_placenta_predo + 
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(),
                     axis.title.y = element_blank(),
                     plot.margin = margin(l = 1)  ),
          nrow = 1)

to the top

comparison PREDO & ITU in predictors

placenta

```r
Placenta_Preds <- rbind(Placenta_Preds_ITU, Placenta_Preds_PREDO)

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continuous predictors, t-test

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```r
placenta_pred_t <- Placenta_Preds %>% 
  select_if(is.numeric) %>%
  map_df(~ broom::tidy(t.test(. ~ Placenta_Preds$group)), .id = 'var')

placenta_pred_t 
t.test(maternal_age ~ group, data=Placenta_Preds)$estimate
t.test(maternal_BMI ~ group, data=Placenta_Preds)$estimate
t.test(birth_weight ~ group, data=Placenta_Preds)$estimate
t.test(birth_length ~ group, data=Placenta_Preds)$estimate
p.adjust(placenta_pred_t$p.value, method = "bonferroni", n = 15)

categorical

placenta_pred_chi <- Placenta_Preds %>% 
  select_if(is.factor) %>%
  map_df(~ broom::tidy(chisq.test(. ,Placenta_Preds$group, correct=F)), .id = 'var')

placenta_pred_chi
p.adjust(placenta_pred_chi$p.value, method = "bonferroni", n = 15)
table(Placenta_Preds$delivery_mode, Placenta_Preds$group)
table(Placenta_Preds$hypertension, Placenta_Preds$group)
table(Placenta_Preds$diabetes, Placenta_Preds$group)
table(Placenta_Preds$smoking, Placenta_Preds$group)

cordblood EPIC

```r
Cordblood_Preds <- rbind(Cordblood_Preds_ITU, Cordblood_Preds_PREDO)

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continuous predictors, t-test

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```r
cordblood_pred_t <- Cordblood_Preds %>% 
  select_if(is.numeric) %>%
  map_df(~ broom::tidy(t.test(. ~ Cordblood_Preds$group)), .id = 'var')

cordblood_pred_t 
# maternal age, maternal BMI
t.test(maternal_age ~ group, data=Cordblood_Preds)$estimate
t.test(maternal_BMI ~ group, data=Cordblood_Preds)$estimate
p.adjust(cordblood_pred_t$p.value, method = "bonferroni", n = 15)
# only maternal age

categorical

cordblood_pred_chi <- Cordblood_Preds %>% 
  select_if(is.factor) %>%
  map_df(~ broom::tidy(chisq.test(. ,Cordblood_Preds$group, correct=F)), .id = 'var')

cordblood_pred_chi
# parity, hypertension, smoking
p.adjust(cordblood_pred_chi$p.value, method = "bonferroni", n = 15)
# only hypertension
table(Cordblood_Preds$delivery_mode, Cordblood_Preds$group)
table(Cordblood_Preds$hypertension, Cordblood_Preds$group)
table(Cordblood_Preds$diabetes, Cordblood_Preds$group)
table(Cordblood_Preds$smoking, Cordblood_Preds$group)

cordblood 450K

```r
Cordblood_Preds450K <- rbind(Cordblood_Preds_ITU, Cordblood_Preds450K_PREDO)

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continuous predictors, t-test

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```r
cordblood_pred450K_t <- Cordblood_Preds450K %>% 
  select_if(is.numeric) %>%
  map_df(~ broom::tidy(t.test(. ~ Cordblood_Preds450K$group)), .id = 'var')

cordblood_pred450K_t 
# maternal age and BMI
t.test(maternal_age ~ group, data=Cordblood_Preds450K)$estimate
t.test(maternal_BMI ~ group, data=Cordblood_Preds450K)$estimate
p.adjust(cordblood_pred450K_t$p.value, method = "bonferroni", n = 15)

categorical

cordblood_pred450K_chi <- Cordblood_Preds450K %>% 
  select_if(is.factor) %>%
  map_df(~ broom::tidy(chisq.test(. ,Cordblood_Preds450K$group, correct=F)), .id = 'var')

cordblood_pred450K_chi
# parity, hypertension, diabetes, alcohol
p.adjust(cordblood_pred450K_chi$p.value, method = "bonferroni", n = 15)
# only parity, hypertension
table(Cordblood_Preds450K$parity, Cordblood_Preds450K$group)
table(Cordblood_Preds450K$hypertension, Cordblood_Preds450K$group)
table(Cordblood_Preds450K$diabetes, Cordblood_Preds450K$group)
table(Cordblood_Preds450K$alcohol, Cordblood_Preds450K$group)

to the top

Predictors correlations

Fig. 2

ITU: look at predictors, in full data (all persons)

ifelse(!dir.exists(file.path(getwd(), "Results/Figures/predictors_cors")), dir.create(file.path(getwd(), "Results/Figures/predictors_cors")), FALSE)
```r
Input_ITU_all <- Data_ITU_all[ ,!(names(Data_ITU_all) %in% c(\Sample_Name\, \PC1_ethnicity\, \PC2_ethnicity\))]
names(Input_ITU_all) <- c(\child sex\, \maternal age\, \maternal smooking\, \delivery mode\, \maternal BMI\, \birth weight\, \birth length\, \head circumference\, \Parity\, \induced labor\, \maternal hypertension\, \maternal diabetes\, \maternal mental disorders\, \maternal alcohol use\)

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```r
```r
Input_M_all <- model.matrix(~0+., data=Input_ITU_all)
colnames(Input_M_all) <- c(\male\,\female\, \maternal age\, \maternal smoking\, \delivery mode\, \maternal BMI\, \birth weight\, \birth length\, \head circumference\, \parity\, \induced labor\, \maternal hypertension\, \maternal diabetes\, \maternal mental disorders\, \maternal alcohol use\)

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```r
Input_M_all %>%
  cor(use="pairwise.complete.obs") %>% 
  corrplot(type="upper", tl.col="black")
png("Results/Figures/predictors_cors/ITU_all.png", width=1600, height= 1500, res=350)
Input_M_all %>%
  cor(use="pairwise.complete.obs") %>% 
  corrplot(type="upper", tl.col="black")
  theme(plot.margin=unit(c(-0.30,0,0,0), "null")) # remove margin around plot
dev.off()
corr.test(Input_ITU_all[6:8])

to the top

PREDO: look at predictors, in full data (all persons)

```r
Input_PREDO_EPIC_all <- Data_PREDO_EPIC_all[ ,!(names(Data_PREDO_EPIC_all) %in% c(\Sample_Name\, \PC1\, \PC2\))]
names(Input_PREDO_EPIC_all) <- c(\child sex\, \maternal age\, \maternal smooking\, \delivery mode\, \maternal BMI\, \birth weight\, \birth length\, \head circumference\, \parity\, \induced labor\, \maternal hypertension\, \maternal diabetes\, \maternal mental disorders\, \maternal alcohol use\)

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```r
```r
Input_M_PREDO_EPIC_all <- model.matrix(~0+., data=Input_PREDO_EPIC_all)
colnames(Input_M_PREDO_EPIC_all) <- c(\male\,\female\, \maternal age\, \maternal smoking\, \delivery mode\, \maternal BMI\, \birth weight\, \birth length\, \head circumference\, \parity\, \induced labor\, \maternal hypertension\, \maternal diabetes\, \maternal mental disorders\, \maternal alcohol use\)

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```r
Input_M_PREDO_EPIC_all %>%
  cor(use="pairwise.complete.obs") %>% 
  corrplot(type="upper", tl.col="black")
png("Results/Figures/predictors_cors/PREDO_EPIC_all.png", width=1600, height= 1500, res=350)
Input_M_PREDO_EPIC_all %>%
  cor(use="pairwise.complete.obs") %>% 
  corrplot(type="upper", tl.col="black")
dev.off()
# mar = c(0, 0, 0, 2)
corr.test(Input_PREDO_EPIC_all[6:8])

correlation DNAmGA-GA

Additional file 7, Table 2

ITU: gestational age epigenetic age correlation (separate for every tissue)

ifelse(!dir.exists(file.path(getwd(), "Results/Figures/corDNAmGAGA")), dir.create(file.path(getwd(), "Results/Figures/corDNAmGAGA")), FALSE)

CVS
Lee clock

cor.test(Data_CVS_ITU$DNAmGA_Lee, Data_CVS_ITU$gestage_at_CVS_weeks, method="pearson")


corCVSGA_Lee <- ggscatter(Data_CVS_ITU, x = "gestage_at_CVS_weeks", y = "DNAmGA_Lee", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at sampling (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="CVS", subtitle="Lee clock")

plotCVSGA_Lee <- ggplot(Data_CVS_ITU, aes(x =gestage_at_CVS_weeks, y =DNAmGA_Lee))+ 
  geom_point(shape=1)+
  xlab("gestational age at sampling (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("CVS \nLee clock")

grid.arrange(corCVSGA_Lee, plotCVSGA_Lee, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Lee_CVS_ITU.tiff", units="in", width=8, height=5, res=300)
corCVSGA_Lee
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Lee_CVS_ITU.tiff", units="in", width=8, height=5, res=300)
plotCVSGA_Lee
dev.off()

Mayne clock:

cor.test(Data_CVS_ITU$DNAmGA_Mayne, Data_CVS_ITU$gestage_at_CVS_weeks, method="pearson")

corCVSGA_Mayne <- ggscatter(Data_CVS_ITU, x = "gestage_at_CVS_weeks", y = "DNAmGA_Mayne", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at sampling (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title=" CVS", subtitle="Mayne clock")

plotCVSGA_Mayne <- ggplot(Data_CVS_ITU, aes(x =gestage_at_CVS_weeks, y =DNAmGA_Mayne))+ 
  geom_point(shape=1)+
  xlab("gestational age at sampling (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("CVS \nMayne clock")

grid.arrange(corCVSGA_Mayne, plotCVSGA_Mayne, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Mayne_CVS_ITU.tiff", units="in", width=8, height=5, res=300)
corCVSGA_Mayne
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Mayne_CVS_ITU.tiff", units="in", width=8, height=5, res=300)
plotCVSGA_Mayne
dev.off()

Cordblood
Knight clock

cor.test(Data_Cord_ITU$DNAmGA_Knight, Data_Cord_ITU$Gestational_Age_Weeks, method="pearson")

corCordGA_Knight <- ggscatter(Data_Cord_ITU, x = "Gestational_Age_Weeks", y = "DNAmGA_Knight", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at birth (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Cordblood", subtitle="Knight clock")

plotCordGA_Knight <- ggplot(Data_Cord_ITU, aes(x =Gestational_Age_Weeks, y =DNAmGA_Knight))+ 
  geom_point(shape=1)+
  xlab("gestational age at birth (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Cordblood \nKnight clock")

grid.arrange(corCordGA_Knight, plotCordGA_Knight, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Cord_Knight_ITU.tiff", units="in", width=8, height=5, res=300)
corCordGA_Knight
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Cord_Knight_ITU.tiff", units="in", width=8, height=5, res=300)
plotCordGA_Knight
dev.off()

## Knight Testing Data set correlation: r=0.91; individual test sets r=0.52 & 0.65)
## Girchenko correlation r=0.51
## Palma-Gudiel: r=0.76
## Suarez: r=.0.52

Bohlin Clock

cor.test(Data_Cord_ITU$DNAmGA_Bohlin, Data_Cord_ITU$Gestational_Age_Weeks, method="pearson")

corCordGA_Bohlin <- ggscatter(Data_Cord_ITU, x = "Gestational_Age_Weeks", y = "DNAmGA_Bohlin", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at birth (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Cordblood", subtitle="Bohlin clock")

plotCordGA_Bohlin <- ggplot(Data_Cord_ITU, aes(x = Gestational_Age_Weeks, y =DNAmGA_Bohlin))+ 
  geom_point(shape=1)+
  xlab("gestational age at birth (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Cordblood \nBohlin clock")

grid.arrange(corCordGA_Bohlin, plotCordGA_Bohlin, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Cord_Bohlin_ITU.tiff", units="in", width=8, height=5, res=300)
corCordGA_Bohlin
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Cord_Bohlin_ITU.tiff", units="in", width=8, height=5, res=300)
plotCordGA_Bohlin
dev.off()

## Simpkin correlation in ALSPAC r=0.65

Placenta
Lee Clock

cor.test(Data_Placenta_ITU$DNAmGA_Lee, Data_Placenta_ITU$Gestational_Age_Weeks, method="pearson")

corPlacentaGA_Lee <- ggscatter(Data_Placenta_ITU, x = "Gestational_Age_Weeks", y = "DNAmGA_Lee", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at birth (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Placenta", subtitle="Lee clock")

plotPlacentaGA_Lee <- ggplot(Data_Placenta_ITU, aes(x =Gestational_Age_Weeks, y=DNAmGA_Lee))+ 
  geom_point(shape=1)+
  xlab("gestational age at birth (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Placenta \nLee clock")

grid.arrange(corPlacentaGA_Lee, plotPlacentaGA_Lee, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Placenta_Lee_ITU.tiff", units="in", width=8, height=5, res=300)
corPlacentaGA_Lee
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Placenta_Lee_ITU.tiff", units="in", width=8, height=5, res=300)
plotPlacentaGA_Lee
dev.off()

Mayne Clock

cor.test(Data_Placenta_ITU$DNAmGA_Mayne, Data_Placenta_ITU$Gestational_Age_Weeks, method="pearson")

corPlacentaGA_Mayne <- ggscatter(Data_Placenta_ITU, x = "Gestational_Age_Weeks", y = "DNAmGA_Mayne", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at birth (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Placenta", subtitle="Mayne clock")

plotPlacentaGA_Mayne <- ggplot(Data_Placenta_ITU, aes(x =Gestational_Age_Weeks, y =DNAmGA_Mayne))+ 
  geom_point(shape=1)+
  xlab("gestational age at birth (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Placenta \nMayne")

grid.arrange(corPlacentaGA_Mayne, plotPlacentaGA_Mayne, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Placenta_Mayne_ITU.tiff", units="in", width=8, height=5, res=300)
corPlacentaGA_Mayne
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Placenta_Mayne_ITU.tiff", units="in", width=8, height=5, res=300)
plotPlacentaGA_Mayne 
dev.off()

to the top

PREDO: gestational age epigenetic age correlation (separate for every tissue)

450K Cordblood Knight with the full estimator, Knight

cor.test(Data_PREDO_450Kcord$DNAmGA_Knight, Data_PREDO_450Kcord$Gestational_Age, method="pearson")

corCord_Knight_P450 <- ggscatter(Data_PREDO_450Kcord, x = "Gestational_Age", y = "DNAmGA_Knight", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at sampling (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Cordblood (450K)", subtitle="Knight clock")

plotCord_Knight_P450 <- ggplot(Data_PREDO_450Kcord, aes(x =Gestational_Age, y =DNAmGA_Knight))+ 
  geom_point(shape=1)+
  xlab("gestational age at sampling (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Cordblood (450K) \nKnight clock")

grid.arrange(corCord_Knight_P450, plotCord_Knight_P450, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Cord450K_Knight_PREDO.tiff", units="in", width=8, height=5, res=300)
corCord_Knight_P450
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Cord450K_Knight_PREDO.tiff", units="in", width=8, height=5, res=300)
plotCord_Knight_P450 
dev.off()
#Data_PREDO_450Kcord[which.min(Data_PREDO_450Kcord$Gestational_Age),] #(visual) outlier, row 70
# exclude this outlier to see what correlation would be then
cor.test(Data_PREDO_450Kcord$DNAmGA_Knight[-70], Data_PREDO_450Kcord$Gestational_Age[-70], method="pearson")

Data_PREDO_450Kcord_outout <- Data_PREDO_450Kcord[-70, ]
ggscatter(Data_PREDO_450Kcord_outout, x = "Gestational_Age", y = "DNAmGA_Knight", 
         add = "reg.line", conf.int = TRUE, 
         cor.coef = TRUE, cor.method = "pearson",
         xlab = "gestational age at sampling (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Cordblood (450K)", subtitle="with outlier removed")

Bohlin with the full estimator

cor.test(Data_PREDO_450Kcord$DNAmGA_Bohlin, Data_PREDO_450Kcord$Gestational_Age, method="pearson")

corCord_Bohlin_P450 <- ggscatter(Data_PREDO_450Kcord, x = "Gestational_Age", y = "DNAmGA_Bohlin", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at sampling (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Cordblood (450K)", subtitle="Bohlin clock")

plotCord_Bohlin_P450 <- ggplot(Data_PREDO_450Kcord, aes(x =Gestational_Age, y =DNAmGA_Bohlin))+ 
  geom_point(shape=1)+
  xlab("gestational age at sampling (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Cordblood (450K) \nBohlin")

grid.arrange(corCord_Bohlin_P450, plotCord_Bohlin_P450, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Cord450K_Bohlin_PREDO.tiff", units="in", width=10, height=5, res=300)
corCord_Bohlin_P450
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Cord450K_Bohlin_PREDO.tiff", units="in", width=10, height=5, res=300)
plotCord_Bohlin_P450
dev.off()

EPIC Cordblood
Knight

cor.test(Data_PREDO_EPICcord$DNAmGA_Knight, Data_PREDO_EPICcord$Gestational_Age, method="pearson")

corCord_Knight_P <- ggscatter(Data_PREDO_EPICcord, x = "Gestational_Age", y = "DNAmGA_Knight", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at sampling (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Cordblood (EPIC)", subtitle="Knight clock")

plotCord_Knight_P <- ggplot(Data_PREDO_EPICcord, aes(x =Gestational_Age, y =DNAmGA_Knight))+ 
  geom_point(shape=1)+
  xlab("gestational age at sampling (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Cordblood (EPIC) \nKnight clock")

grid.arrange(corCord_Knight_P, plotCord_Knight_P, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Cord_Knight_PREDO.tiff", units="in", width=10, height=5, res=300)
corCord_Knight_P
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Cord_Knight_PREDO.tiff", units="in", width=10, height=5, res=300)
plotCord_Knight_P
dev.off()

Bohlin:

cor.test(Data_PREDO_EPICcord$DNAmGA_Bohlin, Data_PREDO_EPICcord$Gestational_Age, method="pearson")

corCord_Bohlin_P <- ggscatter(Data_PREDO_EPICcord, x = "Gestational_Age", y = "DNAmGA_Bohlin", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at sampling (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Cordblood (EPIC)", subtitle="Bohlin clock")

plotCord_Bohlin_P <- ggplot(Data_PREDO_EPICcord, aes(x =Gestational_Age, y =DNAmGA_Bohlin))+ 
  geom_point(shape=1)+
  xlab("gestational age at sampling (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Cordblood (EPIC) \nBohlin")

grid.arrange(corCord_Bohlin_P, plotCord_Bohlin_P, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Cord_Bohlin_PREDO.tiff", units="in", width=10, height=5, res=300)
corCord_Bohlin_P
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Cord_Bohlin_PREDO.tiff", units="in", width=10, height=5, res=300)
plotCord_Bohlin_P
dev.off()

EPIC Placenta
Lee

cor.test(Data_PREDO_EPICplacenta$DNAmGA_Lee, Data_PREDO_EPICplacenta$Gestational_Age, method="pearson")

corPlacenta_Lee_P <- ggscatter(Data_PREDO_EPICplacenta, x = "Gestational_Age", y = "DNAmGA_Lee", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at sampling (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Placenta (EPIC)", subtitle="Lee clock")

plotPlacenta_Lee_P <- ggplot(Data_PREDO_EPICplacenta, aes(x =Gestational_Age, y =DNAmGA_Lee))+ 
  geom_point(shape=1)+
  xlab("gestational age at sampling (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Placenta (EPIC) \nLee clock")

grid.arrange(corPlacenta_Lee_P, plotPlacenta_Lee_P, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Placenta_Lee_PREDO.tiff", units="in", width=10, height=5, res=300)
corPlacenta_Lee_P
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Placenta_Lee_PREDO.tiff", units="in", width=10, height=5, res=300)
plotPlacenta_Lee_P
dev.off()

Mayne

cor.test(Data_PREDO_EPICplacenta$DNAmGA_Mayne, Data_PREDO_EPICplacenta$Gestational_Age, method="pearson")

corPlacenta_Mayne_P <- ggscatter(Data_PREDO_EPICplacenta, x = "Gestational_Age", y = "DNAmGA_Mayne", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at sampling (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Placenta (EPIC)", subtitle="Mayne clock")

plotPlacenta_Mayne_P <- ggplot(Data_PREDO_EPICplacenta, aes(x =Gestational_Age, y =DNAmGA_Mayne))+ 
  geom_point(shape=1)+
  xlab("gestational age at sampling (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Placenta (EPIC) \nMayne")

grid.arrange(corPlacenta_Mayne_P, plotPlacenta_Mayne_P, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Placenta_Mayne_PREDO.tiff", units="in", width=10, height=5, res=300)
corPlacenta_Mayne_P
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Placenta_Mayne_PREDO.tiff", units="in", width=10, height=5, res=300)
plotPlacenta_Mayne_P
dev.off()

to the top

DNAmGA GA correlation plots

for Additional File 7

cor_bohlin_itu <- ggscatter(Data_Cord_ITU, x = "Gestational_Age_Weeks", y = "DNAmGA_Bohlin", 
          add = "reg.line", conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Bohlin (weeks)", subtitle="ITU (n=426)")+
   stat_cor(label.x = 28, label.y=43,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank())+
  scale_y_continuous(limits = c(32,44), breaks = seq(32,44, by=2))+
 scale_x_continuous(limits = c(28,44), breaks = seq(28,44, by=2))


cor_bohlin_predo <- ggscatter(Data_PREDO_EPICcord, x = "Gestational_Age", y = "DNAmGA_Bohlin", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Bohlin Clock (weeks)", subtitle="PREDO 450K (n=149)")+
   stat_cor(label.x = 30, label.y=43,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(32,44), breaks = seq(32,44, by=2))+
  scale_x_continuous(limits = c(30,44), breaks = seq(30,44, by=2))
  
cor_bohlin_predo_450k <- ggscatter(Data_PREDO_450Kcord, x = "Gestational_Age", y = "DNAmGA_Bohlin", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Bohlin Clock (weeks)", subtitle="PREDO EPIC (n=793)")+
   stat_cor(label.x = 26, label.y=43,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(32,44), breaks = seq(32,44, by=2))+
  scale_x_continuous(limits = c(26,44), breaks = seq(26,44, by=2))

Bohlin_DNAmGA_GA <- ggarrange(
          cor_bohlin_itu +
           theme(plot.margin = margin(r = 0.2)),
          cor_bohlin_predo +
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(), axis.title.y = element_blank(), plot.margin = margin(r = 0.2, l = 0.2)),
          cor_bohlin_predo_450k +
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(), axis.title.y = element_blank(), plot.margin = margin(r = 0.2, l = 0.2)),
          nrow = 1,
          align = c("hv"))

# Annotate the figure by adding a common labels
annotate_figure(Bohlin_DNAmGA_GA,
                bottom = text_grob("Gestational Age (weeks)", size = 12))
png(file="Results/Figures/corDNAmGAGA/Bohlin.png", width= 3600, height=2100, res=480)
annotate_figure(Bohlin_DNAmGA_GA,
                bottom = text_grob("Gestational Age (weeks)", size = 12))
dev.off()
cor_knight_itu <- ggscatter(Data_Cord_ITU, x = "Gestational_Age_Weeks", y = "DNAmGA_Knight", 
          add = "reg.line", conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Knight Clock (weeks)", subtitle="ITU (n=426)")+
   stat_cor(label.x = 28, label.y=48,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank())+
  scale_y_continuous(limits = c(28,48), breaks = seq(28,48, by=2))+
 scale_x_continuous(limits = c(28,44), breaks = seq(28,44, by=2))


cor_knight_predo <- ggscatter(Data_PREDO_EPICcord, x = "Gestational_Age", y = "DNAmGA_Knight", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Knight Clock (weeks)", subtitle="PREDO EPIC (n=149)")+
   stat_cor(label.x = 30, label.y=48,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(28,48), breaks = seq(28,48, by=2))+
  scale_x_continuous(limits = c(30,44), breaks = seq(30,44, by=2))
  
cor_knight_predo_450k <- ggscatter(Data_PREDO_450Kcord, x = "Gestational_Age", y = "DNAmGA_Knight", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Knight Clock (weeks)", subtitle="PREDO 450K (n=793)")+
   stat_cor(label.x = 26, label.y=48,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(28,48), breaks = seq(28,48, by=2))+
  scale_x_continuous(limits = c(26,44), breaks = seq(26,44, by=2))

Knight_DNAmGA_GA <- ggarrange(
          cor_knight_itu +
           theme(legend.position="none", plot.margin = margin(r = 0.2) ),
          cor_knight_predo +
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(), axis.title.y = element_blank(), plot.margin = margin(r = 0.2, l = 0.2)),
          cor_knight_predo_450k +
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(), axis.title.y = element_blank(), plot.margin = margin(r = 0.2, l = 0.2)),
          nrow = 1,
          align = c("hv"))

# Annotate the figure by adding a common labels
annotate_figure(Knight_DNAmGA_GA,
                bottom = text_grob("Gestational Age (weeks)", size = 12))
png(file="Results/Figures/corDNAmGAGA/Knight.png", width= 3600, height=2100, res=480)
annotate_figure(Knight_DNAmGA_GA,
                bottom = text_grob("Gestational Age (weeks)", size = 12))
dev.off()
cor_mayne_itu_cvs <- ggscatter(Data_CVS_ITU, x = "gestage_at_CVS_weeks", y = "DNAmGA_Mayne", 
          add = "reg.line", conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Mayne Clock (weeks)", subtitle="ITU CVS (n=264)")+
   stat_cor(label.x = 10, label.y=20,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_text(size=12),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank())+
  scale_y_continuous(limits = c(4,20), breaks = seq(4,20, by=2))+
 scale_x_continuous(limits = c(10,16), breaks = seq(10,16, by=2))


cor_mayne_itu <- ggscatter(Data_Placenta_ITU, x = "Gestational_Age_Weeks", y = "DNAmGA_Mayne", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Mayne Clock (weeks)", subtitle="ITU (n=486)")+
   stat_cor(label.x = 28, label.y=38,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(25,38), breaks = seq(26,38, by=2))+
  scale_x_continuous(limits = c(28,44), breaks = seq(28,44, by=2))
  
cor_mayne_predo <- ggscatter(Data_PREDO_EPICplacenta, x = "Gestational_Age", y = "DNAmGA_Mayne", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Mayne Clock (weeks)", subtitle="PREDO (n=139)")+
   stat_cor(label.x = 32, label.y=38,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(25,38), breaks = seq(26,38, by=2))+
  scale_x_continuous(limits = c(32,44), breaks = seq(32,44, by=2))

Mayne_DNAmGA_GA <- ggarrange(
          cor_mayne_itu +
           theme(legend.position="none", plot.margin = margin(r = 0.2) ),
          cor_mayne_predo +
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(), axis.title.y = element_blank(), plot.margin = margin(r = 0.2, l = 0.2)),
          nrow = 1,
          align = c("hv"))

# Annotate the figure by adding a common labels
annotate_figure(Mayne_DNAmGA_GA,
                bottom = text_grob("Gestational Age (weeks)", size = 12))
png(file="Results/Figures/corDNAmGAGA/Mayne.png", width= 2400, height=2100, res=480)
annotate_figure(Mayne_DNAmGA_GA,
                bottom = text_grob("Gestational Age (weeks)", size = 12))
dev.off()

png(file="Results/Figures/corDNAmGAGA/Mayne_CVS.png", width= 800, height=1400, res=320)
cor_mayne_itu_cvs
dev.off()
cor_lee_itu_cvs <- ggscatter(Data_CVS_ITU, x = "gestage_at_CVS_weeks", y = "DNAmGA_Lee", 
          add = "reg.line", conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Lee Clock (weeks)", subtitle="ITU CVS (n=264)")+
   stat_cor(label.x = 10, label.y=20,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_text(size=12),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank())+
  scale_y_continuous(limits = c(4,20), breaks = seq(4,20, by=2))+
 scale_x_continuous(limits = c(10,16), breaks = seq(10,16, by=2))


cor_lee_itu <- ggscatter(Data_Placenta_ITU, x = "Gestational_Age_Weeks", y = "DNAmGA_Lee", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Lee Clock (weeks)", subtitle="ITU (n=486)")+
   stat_cor(label.x = 28, label.y=44,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(30,44), breaks = seq(30,44, by=2))+
  scale_x_continuous(limits = c(28,44), breaks = seq(28,44, by=2))
  
cor_lee_predo <- ggscatter(Data_PREDO_EPICplacenta, x = "Gestational_Age", y = "DNAmGA_Lee", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Lee Clock (weeks)", subtitle="PREDO (n=139)")+
   stat_cor(label.x = 32, label.y=44,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(30,44), breaks = seq(30,44, by=2))+
  scale_x_continuous(limits = c(32,44), breaks = seq(32,44, by=2))

Lee_DNAmGA_GA <- ggarrange(
          cor_lee_itu +
           theme(legend.position="none", plot.margin = margin(r = 0.2) ),
          cor_lee_predo +
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(), axis.title.y = element_blank(), plot.margin = margin(r = 0.2, l = 0.2)),
          nrow = 1,
          align = c("hv"))

# Annotate the figure by adding a common labels
annotate_figure(Lee_DNAmGA_GA,
                bottom = text_grob("Gestational Age (weeks)", size = 12))
png(file="Results/Figures/corDNAmGAGA/Lee.png", width= 2400, height=2100, res=480)
annotate_figure(Lee_DNAmGA_GA,
                bottom = text_grob("Gestational Age (weeks)", size = 12))
dev.off()

png(file="Results/Figures/corDNAmGAGA/Lee_CVS.png", width= 800, height=1400, res=320)
cor_lee_itu_cvs
dev.off()

Correlation Clocks

correlation cordblood clocks

ifelse(!dir.exists(file.path(getwd(), "Results/Figures/corClocks")), dir.create(file.path(getwd(), "Results/Figures/corClocks")), FALSE)
cor_cord_clocks_itu <- 
  ggscatter(Data_Cord_ITU, x = "DNAmGA_Knight", y = "DNAmGA_Bohlin", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "DNAmGA estimated by the Knight Clock", ylab = "DNAmGA estimated by the Bohlin Clock (weeks)", subtitle="ITU (n=426)")+
   stat_cor(label.x = 30, label.y=43,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(32,44), breaks = seq(32, 44, by=2))+
  scale_x_continuous(limits = c(30,44), breaks = seq(30, 44, by=2))
  #coord_cartesian(ylim = c(32,43))

cor_cord_clocks_predo <-ggscatter(Data_PREDO_EPICcord, x = "DNAmGA_Knight", y = "DNAmGA_Bohlin", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "DNAmGA estimated by the Knight Clock", ylab = "DNAmGA estimated by the Bohlin Clock", subtitle="PREDO EPIC (n=149)")+
   stat_cor(label.x = 30,label.y=43, p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_blank(), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(32,44), breaks = seq(32, 44, by=2))+
  scale_x_continuous(limits = c(30,44), breaks = seq(30, 44, by=2))
 # coord_cartesian(ylim = c(32,43))

cor_cord_clocks_predo_450k <- ggscatter(Data_PREDO_450Kcord, x = "DNAmGA_Knight", y = "DNAmGA_Bohlin",
          add = "reg.line", conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = "pearson",
          xlab = "DNAmGA estimated by the Knight Clock", ylab = "DNAmGA estimated by the Bohlin Clock", subtitle="PREDO 450K (n=795)")+
   stat_cor(label.x = 30, label.y=43,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_blank(), axis.title.x=element_blank(), legend.title = element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(32,44), breaks = seq(32, 44, by=2))+
  scale_x_continuous(breaks = seq(30, 44, by=2))
 # coord_cartesian(ylim = c(32,43))

#ggarrange(grobs=cor_cord_clocks_itu, cor_cord_clocks_predo, cor_cord_clocks_predo_450k, nrow=1, align=c("hv"), top="Correlation Cord blood Clocks")

clock_cord_cor_gg <- ggarrange(
          cor_cord_clocks_itu +
           theme(legend.position="none", plot.margin = margin(r = 0.2) ),
          cor_cord_clocks_predo +
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(), axis.title.y = element_blank(), plot.margin = margin(r = 0.2, l = 0.2)),
          cor_cord_clocks_predo_450k +
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(),
                     plot.margin = margin(l = 0.2)),
          nrow = 1,
          align = c("hv"))

# Annotate the figure by adding a common labels
cor_clock_cor <- annotate_figure(clock_cord_cor_gg,
                bottom = text_grob("DNAmGA estimated by the Knight Clock (weeks)", size = 12), top = text_grob("Correlation Cord blood Clocks \n", size = 14))
png(file="Results/Figures/corClocks/cord.png", width= 3600, height=2100, res=480)
annotate_figure(clock_cord_cor_gg,
                bottom = text_grob("DNAmGA estimated by the Knight Clock (weeks)", size = 12))
dev.off()

to the top

correlation placenta clocks

cor_placenta_clocks_itu <- ggscatter(Data_Placenta_ITU, x = "DNAmGA_Mayne", y = "DNAmGA_Lee", 
          add = "reg.line", conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = "pearson",
          xlab = "DNAmGA estimated by the Mayne Clock", ylab = "DNAmGA estimated by the Lee Clock (weeks)", subtitle="ITU (n=486)")+
   stat_cor(label.x = 25, label.y=43,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(30,44), breaks = seq(30,44, by=2))+
  scale_x_continuous(limits = c(25,40), breaks = seq(26,40, by=2))


cor_placenta_clocks_predo <- ggscatter(Data_PREDO_EPICplacenta, x = "DNAmGA_Mayne", y = "DNAmGA_Lee", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "DNAmGA estimated by the Lee Clock", ylab = "DNAmGA estimated by the Mayne Clock", subtitle="PREDO (n=139)")+
   stat_cor(label.x = 26, label.y=43,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(30,44), breaks = seq(30,44, by=2))+
  scale_x_continuous(limits = c(26,36), breaks = seq(26,36, by=2))

clock_placenta_cor_gg <- ggarrange(
          cor_placenta_clocks_itu +
           theme(legend.position="none", plot.margin = margin(r = 0.2) ),
          cor_placenta_clocks_predo +
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(), axis.title.y = element_blank(), plot.margin = margin(r = 0.2, l = 0.2)),
          nrow = 1,
          align = c("hv"))

# Annotate the figure by adding a common labels
pla_clock_cor <- annotate_figure(clock_placenta_cor_gg,
                bottom = text_grob("DNAmGA estimated by the Mayne Clock (weeks)", size = 12), top = text_grob("Correlation Placenta Clocks \n", size = 14))
png(file="Results/Figures/corClocks/placenta.png", width= 2400, height=1400, res=320)
annotate_figure(clock_placenta_cor_gg,
                bottom = text_grob("DNAmGA estimated by the Mayne Clock (weeks)", size = 12))
dev.off()
ggscatter(Data_CVS_ITU, x = "Gestational_Age_Weeks", y = "delta_Lee", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age (weeks)", ylab = "delta Lee", title="Correlation CVS gestational age deviance (ITU)")

to the top

EAAR Descriptive

ITU: Visualization EAAR

ifelse(!dir.exists(file.path(getwd(), "Results/Figures/EAAR_descriptive")), dir.create(file.path(getwd(), "Results/Figures/EAAR_descriptive")), FALSE)

CVS

EAARCVS <- ggplot(Data_CVS_ITU, aes(x= gestage_at_CVS_weeks, y= EAAR_Lee, label=Sample_Name))+
  geom_point() +geom_text(aes(label=Sample_Name),hjust=0, vjust=0)+
  xlab("gestational age at sampling (weeks)")+
  xlim(5,20)+
  ylim(-10,10)+
  geom_line(y=0, linetype="dashed")+
  ylab("epigenetic age acceleration residuals \n(Lee clock)")

EAARCVS_sex <- Data_CVS_ITU[!is.na(Data_CVS_ITU$EAAR_Lee), ] %>%
  group_by(Child_Sex) %>%
  mutate(outlier = ifelse(is_outlier(EAAR_Lee), Sample_Name, as.numeric(NA))) %>%
  ggplot(., aes(x = Child_Sex, y = EAAR_Lee)) +
    geom_boxplot() +
    geom_text(size=2.5, aes(label = outlier), na.rm = TRUE, hjust=-0.3)+
  xlab("Child sex")+
  ylab("epigenetic age acceleration residuals \n(Lee clock)")+
  geom_hline(aes(yintercept=0))

EAARCVS_boxplot <- ggplot(Data_CVS_ITU, aes(x=EAAR_Lee))+ geom_histogram(binwidth=0.1)+ labs(x="epigenetic age acceleration residuals (Lee clock)", y = "Count (N = 200)")

cowplot::plot_grid(EAARCVS, EAARCVS_sex, EAARCVS_boxplot)

length(na.omit(Data_CVS_ITU$EAAR_Lee))
# note that 65 rows were removed because they are NA in EAARVS (no ethnicity info)
```r
deltaCVS_boxplot <- ggplot(Data_CVS_ITU, aes(x=delta_Lee))+ geom_histogram(binwidth=0.1)+ labs(x=\epigenetic age acceleration delta (Lee clock)\, y = \Count (N = 200)\)
#deltaCVS_boxplot

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucG5nKGZpbGU9XCJSZXN1bHRzL0ZpZ3VyZXMvRUFBUl9kZXNjcmlwdGl2ZS9DVlMucG5nXCIsd2lkdGg9MjIwMCwgaGVpZ2h0PTE0MDAsIHJlcz0zMDApXG5nZ3Bsb3QoRGF0YV9DVlNfSVRVLCBhZXMoeD1FQUFSX0xlZSkpKyBnZW9tX2hpc3RvZ3JhbShiaW53aWR0aD0wLjEpKyBsYWJzKHg9XCJFQUFSIChMZWUgY2xvY2spXCIsIHkgPSBcIkNvdW50IChuID0gMjAwKVwiKStcbnRoZW1lKHRleHQgPSBlbGVtZW50X3RleHQoc2l6ZSA9IDE1KSwgYXhpcy50aXRsZS54PSBlbGVtZW50X3RleHQoc2l6ZT0xNSksIGF4aXMudGl0bGUueT0gZWxlbWVudF90ZXh0KHNpemU9MTUpKVxuZGV2Lm9mZigpXG5gYGAifQ== -->

```r
png(file="Results/Figures/EAAR_descriptive/CVS.png",width=2200, height=1400, res=300)
ggplot(Data_CVS_ITU, aes(x=EAAR_Lee))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Lee clock)", y = "Count (n = 200)")+
theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
dev.off()

Cordblood

EAARCord <- ggplot(Data_Cord_ITU, aes(x= Gestational_Age_Weeks, y= EAAR_Bohlin, label=Sample_Name))+
  geom_point() +geom_text(aes(label=Sample_Name),hjust=0, vjust=0)+
  xlab("gestational age at birth (weeks)")+
  xlim(25,50)+
  ylim(-10,10)+
  geom_line(y=0, linetype="dashed")+
  ylab("epigenetic age acceleration residuals \nBohlin clock")

EAARCord_sex <- Data_Cord_ITU[!is.na(Data_Cord_ITU$EAAR_Bohlin), ] %>%
  group_by(Child_Sex) %>%
  mutate(outlier = ifelse(is_outlier(EAAR_Bohlin), Sample_Name, as.numeric(NA))) %>%
  ggplot(., aes(x =Child_Sex, y = EAAR_Bohlin)) +
    geom_boxplot() +
    geom_text(size=2.5,aes(label = outlier), na.rm = TRUE, hjust = -0.3)+
  xlab("Child sex")+
  ylab("epigenetic age acceleration residuals \nBohlin clock")+
  geom_hline(aes(yintercept=0))

EAARCord_boxplot <- ggplot(Data_Cord_ITU, aes(x=EAAR_Bohlin))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Bohlin clock)", y = "Count (N = 395)")

cowplot::plot_grid(EAARCord, EAARCord_sex, EAARCord_boxplot)
length(na.omit(Data_Cord_ITU$EAAR_Bohlin))
png(file="Results/Figures/EAAR_descriptive/Cord.png",width=2200, height=1400, res=300)
ggplot(Data_Cord_ITU, aes(x=EAAR_Bohlin))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Bohlin clock)", y = "Count (n = 395)")+
theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
dev.off()
```r
deltaCord_boxplot <- ggplot(Data_Cord_ITU, aes(x=delta_Bohlin))+ geom_histogram(binwidth=0.1)+ labs(x=\delta (Bohlin clock)\, y = \Count (N = 395)\)
#deltaCord_boxplot

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**Placenta**

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```r
EAARPlacenta <- ggplot(Data_Placenta_ITU, aes(x= Gestational_Age_Weeks, y= EAAR_Lee, label=Sample_Name))+
  geom_point() +geom_text(aes(label=Sample_Name),hjust=0, vjust=0)+
  xlab("gestational age at birth (weeks)")+
  xlim(25,50)+
  ylim(-10,10)+
  geom_line(y=0, linetype="dashed")+
  ylab("epigenetic age acceleration residuals \nLee clock")

EAARPlacenta_sex <- Data_Placenta_ITU[!is.na(Data_Placenta_ITU$EAAR_Lee), ] %>%
  group_by(Child_Sex) %>%
  mutate(outlier = ifelse(is_outlier(EAAR_Lee), Sample_Name, as.numeric(NA))) %>%
  ggplot(., aes(x = Child_Sex, y = EAAR_Lee)) +
    geom_boxplot() +
    geom_text(size=2.5,aes(label = outlier), na.rm = TRUE, hjust = -0.3)+
  xlab("Child sex")+
  ylab("epigenetic age acceleration residuals \nLee clock")+
  geom_hline(aes(yintercept=0))

EAARPlacenta_boxplot <- ggplot(Data_Placenta_ITU, aes(x=EAAR_Lee))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Lee clock)", y = "Count (N = 439)")

cowplot::plot_grid(EAARPlacenta, EAARPlacenta_sex, EAARPlacenta_boxplot)
length(na.omit(Data_Placenta_ITU$EAAR_Lee))
png("Results/Figures/EAAR_descriptive/Placenta.png", width=2200, height=1400, res=300)
ggplot(Data_Placenta_ITU, aes(x=EAAR_Lee))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Lee clock)", y = "Count (n = 439)")+
theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
dev.off()
deltaPlacenta_boxplot <- ggplot(Data_Placenta_ITU, aes(x=delta_Lee))+ geom_histogram(binwidth=0.1)+ labs(x="delta (Lee clock)", y = "Count (N = 486)")
deltaPlacenta_boxplot 

to the top

PREDO: Visualization EAAR

450K Cordblood

EAARCord450K <- ggplot(Data_PREDO_450Kcord, aes(x= Gestational_Age, y= EAAR_Bohlin, label=Sample_Name))+
  geom_point() +geom_text(aes(label=Sample_Name),hjust=0, vjust=0)+
  xlab("gestational age at birth (weeks)")+
  xlim(25,50)+
  ylim(-15,15)+
  geom_line(y=0, linetype="dashed")+
  ylab("epigenetic age acceleration residuals \nBohlin clock")

EAARCord450K_sex <- Data_PREDO_450Kcord[!is.na(Data_PREDO_450Kcord$EAAR_Bohlin), ] %>%
  group_by(Child_Sex) %>%
  mutate(outlier = ifelse(is_outlier(EAAR_Bohlin), Sample_Name, as.numeric(NA))) %>%
  ggplot(., aes(x = Child_Sex, y = EAAR_Bohlin)) +
    geom_boxplot() +
    geom_text(size=2.5,aes(label = outlier), na.rm = TRUE, hjust = -0.3)+
  xlab("Child sex")+
  ylab("epigenetic age acceleration residuals \nBohlin clock")+
  geom_hline(aes(yintercept=0))

EAARCord450K_boxplot <- ggplot(Data_PREDO_450Kcord, aes(x=EAAR_Bohlin))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Bohlin clock)", y = "Count (N = 785)")

#cowplot::plot_grid(EAARCord450K, EAARCord450K_sex, EAARCord450K_boxplot)
length(na.omit(Data_PREDO_450Kcord$EAAR_Bohlin))
png("Results/Figures/EAAR_descriptive/Cord450K_PREDO.png", width=2200, height=1400, res=300)
ggplot(Data_PREDO_450Kcord, aes(x=EAAR_Bohlin))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Bohlin clock)", y = "Count (n = 785)")+
theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
dev.off()

EPIC Cordblood

EAARCordEPIC <- ggplot(Data_PREDO_EPICcord, aes(x= Gestational_Age, y= EAAR_Bohlin, label=Sample_Name))+
  geom_point() +geom_text(aes(label=Sample_Name),hjust=0, vjust=0)+
  xlab("gestational age at birth (weeks)")+
  xlim(30,45)+
  ylim(-15,15)+
  geom_line(y=0, linetype="dashed")+
  ylab("epigenetic age acceleration residuals \nBohlin clock")

EAARCordEPIC_sex <- Data_PREDO_EPICcord[!is.na(Data_PREDO_EPICcord$EAAR_Bohlin), ] %>%
  group_by(Child_Sex) %>%
  mutate(outlier = ifelse(is_outlier(EAAR_Bohlin), Sample_Name, as.numeric(NA))) %>%
  ggplot(., aes(x = Child_Sex, y = EAAR_Bohlin)) +
    geom_boxplot() +
    geom_text(aes(label = outlier), na.rm = TRUE, hjust = -0.3)+
  xlab("Child sex")+
  ylab("epigenetic age acceleration residuals \nBohlin clock")+
  geom_hline(aes(yintercept=0))

EAARCordEPIC_boxplot <- ggplot(Data_PREDO_EPICcord, aes(x=EAAR_Bohlin))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Bohlin clock)", y = "Count (N = 146)")

#cowplot::plot_grid(EAARCordEPIC, EAARCordEPIC_sex, EAARCordEPIC_boxplot)
length(na.omit(Data_PREDO_EPICcord$EAAR_Bohlin))
png("Results/Figures/EAAR_descriptive/CordEPIC_PREDO.png", width=2200, height=1400, res=300)
ggplot(Data_PREDO_EPICcord, aes(x=EAAR_Bohlin))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Bohlin clock)", y = "Count (n = 146)")+
theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
dev.off()

EPIC Placenta

EAARPlacentaEPIC <- ggplot(Data_PREDO_EPICplacenta, aes(x= Gestational_Age, y= EAAR_Lee, label=Sample_Name))+
  geom_point() +geom_text(aes(label=Sample_Name),hjust=0, vjust=0)+
  xlab("gestational age at birth (weeks)")+
  xlim(30,45)+
  ylim(-15,15)+
  geom_line(y=0, linetype="dashed")+
  ylab("epigenetic age acceleration residuals \nLee clock")

EAARPlacentaEPIC_sex <- Data_PREDO_EPICplacenta[!is.na(Data_PREDO_EPICplacenta$EAAR_Lee),] %>%
  group_by(Child_Sex) %>%
  #mutate(outlier = ifelse(is_outlier(EAAR_Lee), Sample_Name, as.numeric(NA))) %>%
  ggplot(., aes(x = Child_Sex, y = EAAR_Lee)) +
    geom_boxplot() +
    #geom_text(size=2.5, aes(label = outlier), na.rm = TRUE, hjust = -0.3)+
  xlab("Child sex")+
  ylab("epigenetic age acceleration residuals \nLee clock")+
  geom_hline(aes(yintercept=0))

EAARPlacentaEPIC_boxplot <- ggplot(Data_PREDO_EPICplacenta, aes(x=EAAR_Lee))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Lee clock)", y = "Count (N = 118)")

#cowplot::plot_grid(EAARPlacentaEPIC, EAARPlacentaEPIC_sex, EAARPlacentaEPIC_boxplot)
length(na.omit(Data_PREDO_EPICplacenta$EAAR_Lee))
png("Results/Figures/EAAR_descriptive/PlacentaEPIC_PREDO.png", width=2200, height=1400, res=300)
ggplot(Data_PREDO_EPICplacenta, aes(x=EAAR_Lee))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Lee clock)", y = "Count (n = 118)")+
theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
dev.off()

to the top

Single Tissue Models

ifelse(!dir.exists(file.path(getwd(), "InputData/Data_ElasticNets/")), dir.create(file.path(getwd(), "InputData/Data_ElasticNets/")), FALSE)
ifelse(!dir.exists(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/")), dir.create(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/")), FALSE)
ifelse(!dir.exists(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/Outcome_main/")), dir.create(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/Outcome_main/")), FALSE)
ifelse(!dir.exists(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/Outcome_add/")), dir.create(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/Outcome_add/")), FALSE)
ifelse(!dir.exists(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol")), dir.create(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol")), FALSE)
ifelse(!dir.exists(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split")), dir.create(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split")), FALSE)
ifelse(!dir.exists(file.path(getwd(), "Results/Tables/")), dir.create(file.path(getwd(), "Results/Tables/")), FALSE)
```r
rm(list = setdiff(ls(), lsf.str()))

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**ITU**

## Cord blood elastic net {#elasticnetCordITU}  
main model, without alcohol variable


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```r
```r
# in case you want to start from here
load(\InputData/ClockCalculationsInput/Reg_Input_Data_Cord_ITU_EAAR_noNa_n.Rdata\)

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```r
```r
yrc_mat_ITU_Cord_n <- matrix(Reg_Input_Data_Cord_ITU_EAAR_noNa_n$EAAR_Bohlin)
xrc_mat_ITU_Cord_n <- model.matrix( ~ . - EAAR_Bohlin, data = Reg_Input_Data_Cord_ITU_EAAR_noNa_n)[, -1]
yrc_mat_ITU_scaled_Cord_n <- scale(yrc_mat_ITU_Cord_n)
xrc_mat_ITU_scaled_Cord_n <- scale(xrc_mat_ITU_Cord_n)

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<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Cord_ITU_n <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_ITU_scaled_Cord_n), replace = TRUE) -->
<!--     ensr(xrc_mat_ITU_scaled_Cord_n[rws, ], yrc_mat_ITU_scaled_Cord_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!--   end_time <- Sys.time() -->
<!--   end_time - start_time -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Cord_ITU_n, file="InputData/Data_ElasticNets/bootstraps_Cord_ITU_n_1000.Rdata") -->
<!-- ``` -->



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```r
```r
load(\InputData/Data_ElasticNets/bootstraps_Cord_ITU_n_1000.Rdata\)

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first get a summary of all ensr objects

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```r
summaries_Cord_ITU_n <-
  bootstraps_Cord_ITU_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Cord_ITU_n

The summary method for ensr objects returns a data.table with values of λ, α, the mean cross-validation error cvm, and the number of non-zero coefficients. The l_index is the list index of the ensr object associated with the noted α value.

For each bootstrap, look at the number of non-zero coefficients and the minimum cvm for this number of non-zero coefficients:

summaries_Cord_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()+
  ggplot2::labs(x="\nnzero", y="cvm\n")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))+
  ggplot2::theme_bw()
  

in the “standard” procedure, the preferable model is defined as the model with the minimum cvm (nzero, alpha, lambda etc. are selected from this)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstraps_Cord.png", width=2200, height=1400, res=400)
summaries_Cord_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()+
  ggplot2::labs(x="\nnzero", y="cvm\n")+
  ggplot2::theme(text = element_text(size = 18), axis.title.x= element_text(size=20), axis.title.y= element_text(size=20))+
  ggplot2::theme_bw()
dev.off()

Now a look at the coefficients build a data.table with columns to store the coefficient values for the models with smallest cvm by number of non-zero coefficients (and bootstrap).

```r
load(\InputData/Data_ElasticNets/pm2_Cord_ITU_n.Rdata\)
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap

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look how often a particular variable is associated with a non-zero coefficient in a model with a given number of non-zero coefficients (over all bootstraps)


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```r
csummary_Cord_ITU_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                               list(pm2_Cord_ITU_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"), by = nzero]
                                    ,
                                    pm2_Cord_ITU_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                    pm2_Cord_ITU_n[, .(median_cvm = median(cvm)), by = nzero]
                               ))[order(nzero)]

csummary_Cord_ITU_n

plot the results, in the following graphic the size and color of the points in the top plot indicate how often the variable is in the model with nzero non-zero coefficents

g1_Cord_ITU_n <-
  csummary_Cord_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex", "birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor\n", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
  

g2_Cord_ITU_n <-
  csummary_Cord_ITU_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "nzero")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Cord_ITU_n, g2_Cord_ITU_n, ncol = 1)
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstrapModels_Cord.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Cord_ITU_n, g2_Cord_ITU_n, ncol = 1)
dev.off()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_Cord.png", width=2800, height=1400, res=400)
g1_Cord_ITU_n
dev.off()
elbow_finder(csummary_Cord_ITU_n$nzero, csummary_Cord_ITU_n$median_cvm)

nzero_indices_Cord <- data.frame(t(elbow_finder(csummary_Cord_ITU_n$nzero, csummary_Cord_ITU_n$median_cvm)))
colnames(nzero_indices_Cord) <- c("x", "y")
rownames(nzero_indices_Cord) <- NULL
```r
nzero_final_cord_itu <- 9

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look at models with 9 non-zero coefficient.

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```r
csummary_Cord_ITU_n[nzero %in% nzero_final_cord_itu]
nonzero_choose_Cord <- ggplot2::ggplot(csummary_Cord_ITU_n) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_Cord, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median cvm over bootstraps\n")+
  ggplot2::xlab("\nnumber of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[14], yend = median_cvm[14], colour = "segment"), data = csummary_Cord_ITU_n, show.legend = F)+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
  
nonzero_choose_Cord
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/nzero_choose_Cord.png", width=2200, height=1400, res=400)
nonzero_choose_Cord
dev.off()

look at models with 9 non-zero coefficients. filter for cut-off 75% -> which variables occur in more than 75% of models.

```r
summary_Cord_ITU_n_finalnzero <- csummary_Cord_ITU_n[nzero %in% nzero_final_cord_itu]
sig_var_names_Cord_ITU_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_Cord_ITU_n_finalnzero[,!c(\nzero\, \mean_cvm\, \median_cvm\)]) %>% colnames()
colnames(summary_Cord_ITU_n_finalnzero) <- c(\non-zero\, \child sex (female)\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\, \mean cvm\, \median cvm\)
summary_Cord_ITU_n_finalnzeroT <- as.data.frame(t(summary_Cord_ITU_n_finalnzero[,-c(\non-zero\, \median cvm\, \mean cvm\)]))
summary_Cord_ITU_n_finalnzeroT$variable <- rownames(summary_Cord_ITU_n_finalnzeroT)
rownames(summary_Cord_ITU_n_finalnzeroT) <- NULL
names(summary_Cord_ITU_n_finalnzeroT)[names(summary_Cord_ITU_n_finalnzeroT) == 'V1'] <- 'percent'
summary_Cord_ITU_n_finalnzeroT <- summary_Cord_ITU_n_finalnzeroT[order(summary_Cord_ITU_n_finalnzeroT$percent),]

summary_Cord_ITU_n_finalnzeroT$number <- seq(1, length(summary_Cord_ITU_n_finalnzeroT$variable))

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```r
perc_vars_Cord_ITU_n <- 
  ggplot(summary_Cord_ITU_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("\n% occurence in models with nzero coefficients = 9    ")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("predictor\n")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

perc_vars_Cord_ITU_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Cord_ITU_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/varsPercent_Cord.png", width=2900, height=1400, res=400)
perc_vars_Cord_ITU_n
dev.off()

A metric of interest could be the width of the confidence intervals about a bootstrapped estimate of the coefficient, when the coefficient is non-zero:

pm2_Cord_ITU_n_coef <-
  dcast(pm2_Cord_ITU_n[,
                       as.list(unlist(
                         lapply(.SD,
                                function(x) {
                                  y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                  list("non_zero" = 100 * mean(x != 0),
                                       lcl = y[1],
                                       ucl = y[2],
                                       width = diff(y),
                                       median = median(x[x!= 0]))
                                }))),
                       .SDcols = c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"),
                       by = nzero][order(nzero)] %>%
          melt(id.var = "nzero") %>%
          .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
          .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
          .[nzero ==nzero_final_cord_itu], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_Cord_ITU_n_coef <-
  pm2_Cord_ITU_n_coef[match(c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"), pm2_Cord_ITU_n_coef$variable),]
pm2_Cord_ITU_n_coef$variable <- factor(pm2_Cord_ITU_n_coef$variabl, levels=unique(pm2_Cord_ITU_n_coef$variable))

## NOTE: median is used here instead of mean
# make frame for only significant variables:
pm2_Cord_ITU_n_datable <- dcast(pm2_Cord_ITU_n[,
                                               as.list(unlist(
                                                 lapply(.SD,
                                                        function(x) {
                                                          y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                                          list("non_zero" = 100 * mean(x != 0),
                                                               lcl = y[1],
                                                               ucl = y[2],
                                                               width = diff(y),
                                                               median = median(x[x!= 0]))
                                                        }))),
                                               .SDcols = c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"),
                                               by = nzero][order(nzero)] %>%
                                  melt(id.var = "nzero") %>%
                                  .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
                                  .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
                                  # print %>%
                                  .[nzero == nzero_final_cord_itu & variable %in% sig_var_names_Cord_ITU_n_finalnzero], nzero+ variable ~ metric, value.var="value")

pm2_Cord_ITU_n_datable
```r
write_xlsx(pm2_Cord_ITU_n_coef,\Results/Tables/CoefficientsModel_Cord.xlsx\)

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```r
```r
sig_vars_Cord_ITU_n <-
  pm2_Cord_ITU_n_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x=\nzero\)+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf(\%0.2f\, round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = \purple\, low = \black\, midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c(\child sex (female)\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\))+
  ggplot2::labs(y=\predictor\, x = \number of non-zero coefficients = 9\, color=\%\)

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```r
coef_Cord_ITU_n <- 
  ggplot(pm2_Cord_ITU_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Cord_ITU_n 
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/coef_Cord.png", width=2800, height=1400, res=400)
coef_Cord_ITU_n 
dev.off()
p1 <-
  csummary_Cord_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor\n", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
  ggplot(pm2_Cord_ITU_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
   ggtitle("nzero = 9")+
  theme_bw()+
 theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_coef_Cord.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()

get the beta values

```r
### Code for only including \significant variables\ in the beta vector, based on VIP (>75% not-zero in bootstraps)

# get median beta values of the 1000 bootstraps for the model with 9 non-zero coefficients
Beta_hat_s_cord_n <- matrix(miscTools::colMedians(pm2_Cord_ITU_n[nzero == nzero_final_cord_itu, .SD, .SDcols = c(\(Intercept)\,sig_var_names_Cord_ITU_n_finalnzero)]), ncol = 1)
# intenept and variable beta values
# NOTE that median is used here
rownames(Beta_hat_s_cord_n) <- c(\Intercept\, sig_var_names_Cord_ITU_n_finalnzero)

Beta_Cord_ITU_n <- Beta_hat_s_cord_n

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```r
```r
save(Beta_Cord_ITU_n, file=\InputData/Data_ElasticNets/Beta_Cord_ITU_n.Rdata\)

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[to the top](#top)

## Cord blood elastic net {#elasticnetCordITU_a}  
additional model, with alcohol variable


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```r
```r
# in case you want to start from here
load(\InputData/ClockCalculationsInput/Reg_Input_Data_Cord_ITU_EAAR_noNa_wa.Rdata\)

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```r
```r
yrc_mat_ITU_Cord_wa <- matrix(Reg_Input_Data_Cord_ITU_EAAR_noNa_wa$EAAR_Bohlin)
xrc_mat_ITU_Cord_wa <- model.matrix( ~ . - EAAR_Bohlin, data = Reg_Input_Data_Cord_ITU_EAAR_noNa_wa)[, -1]
yrc_mat_ITU_scaled_Cord_wa <- scale(yrc_mat_ITU_Cord_wa)
xrc_mat_ITU_scaled_Cord_wa <- scale(xrc_mat_ITU_Cord_wa)

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<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Cord_ITU_wa <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_ITU_scaled_Cord_wa), replace = TRUE) -->
<!--     ensr(xrc_mat_ITU_scaled_Cord_wa[rws, ], yrc_mat_ITU_scaled_Cord_wa[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!--   end_time <- Sys.time() -->
<!--   end_time - start_time -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Cord_ITU_wa, file="InputData/Data_ElasticNets/bootstraps_Cord_ITU_wa_1000.Rdata") -->
<!-- ``` -->



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```r
```r
load(\InputData/Data_ElasticNets/bootstraps_Cord_ITU_wa_1000.Rdata\)

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```r
summaries_Cord_ITU_wa <-
  bootstraps_Cord_ITU_wa %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Cord_ITU_wa
summaries_Cord_ITU_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/bootstraps_Cord.png", width=800, height=600)
summaries_Cord_ITU_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```r
load(\InputData/Data_ElasticNets/pm2_Cord_ITU_wa.Rdata\)
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap

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```r
csummary_Cord_ITU_wa <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                               list(pm2_Cord_ITU_wa[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes", "maternal_alcohol_useyes"), by = nzero]
                                    ,
                                    pm2_Cord_ITU_wa[, .(mean_cvm = mean(cvm)), by = nzero],
                                    pm2_Cord_ITU_wa[, .(median_cvm = median(cvm)), by = nzero]
                               ))[order(nzero)]

csummary_Cord_ITU_wa
g1_Cord_ITU_wa <-
  csummary_Cord_ITU_wa %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex", "birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking", "maternal alcohol use"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

g2_Cord_ITU_wa <-
  csummary_Cord_ITU_wa %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Cord_ITU_wa, g2_Cord_ITU_wa, ncol = 1)
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/bootstrapModels_Cord.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Cord_ITU_wa, g2_Cord_ITU_wa, ncol = 1)
dev.off()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/Model_Cord.png", width=2800, height=1400, res=400)
g1_Cord_ITU_wa
dev.off()
elbow_finder(csummary_Cord_ITU_wa$nzero, csummary_Cord_ITU_wa$median_cvm)

nzero_indices_Cord <- data.frame(t(elbow_finder(csummary_Cord_ITU_wa$nzero, csummary_Cord_ITU_wa$median_cvm)))
colnames(nzero_indices_Cord) <- c("x", "y")
rownames(nzero_indices_Cord) <- NULL
```r
nzero_final_cord_wa <- 7

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look at models with final non-zero coefficient.

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```r
csummary_Cord_ITU_wa[nzero %in% nzero_final_cord_wa]
nonzero_choose_Cord <- ggplot2::ggplot(csummary_Cord_ITU_wa) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_Cord, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median of minimum cross-validation errors over bootstraps")+
  ggplot2::xlab("number of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[15], yend = median_cvm[15], colour = "segment"), data = csummary_Cord_ITU_wa, show.legend = F)

nonzero_choose_Cord
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/nzero_choose_Cord.png", width=1600, height=1400, res=300)
nonzero_choose_Cord
dev.off()
```r
summary_Cord_ITU_wa_finalnzero <- csummary_Cord_ITU_wa[nzero %in% nzero_final_cord_wa]
sig_var_names_Cord_ITU_wa_finalnzero <- Filter(function(x) any(x > 0.75), summary_Cord_ITU_wa_finalnzero[,!c(\nzero\, \mean_cvm\, \median_cvm\)]) %>% colnames()
colnames(summary_Cord_ITU_wa_finalnzero) <- c(\non-zero\, \child sex (female)\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\, \maternal alcohol use (yes)\, \mean cvm\, \median cvm\)
summary_Cord_ITU_wa_finalnzeroT <- as.data.frame(t(summary_Cord_ITU_wa_finalnzero[,-c(\non-zero\, \median cvm\, \mean cvm\)]))
summary_Cord_ITU_wa_finalnzeroT$variable <- rownames(summary_Cord_ITU_wa_finalnzeroT)
rownames(summary_Cord_ITU_wa_finalnzeroT) <- NULL
names(summary_Cord_ITU_wa_finalnzeroT)[names(summary_Cord_ITU_wa_finalnzeroT) == 'V1'] <- 'percent'
summary_Cord_ITU_wa_finalzeroT <- summary_Cord_ITU_wa_finalnzeroT[order(summary_Cord_ITU_wa_finalnzeroT$percent),]

summary_Cord_ITU_wa_finalnzeroT$number <- seq(1, length(summary_Cord_ITU_wa_finalnzeroT$variable))

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```r
perc_vars_Cord_ITU_wa <- 
  ggplot(summary_Cord_ITU_wa_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("% occurence in models with nzero coefficients = 8")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("variable")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()

perc_vars_Cord_ITU_wa

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Cord_ITU_wa_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/varsPercent_Cord.png", width=1100, height=1400, res=300)
perc_vars_Cord_ITU_wa
dev.off()
pm2_Cord_ITU_wa_coef <-
  dcast(pm2_Cord_ITU_wa[,
                       as.list(unlist(
                         lapply(.SD,
                                function(x) {
                                  y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                  list("non_zero" = 100 * mean(x != 0),
                                       lcl = y[1],
                                       ucl = y[2],
                                       width = diff(y),
                                       median = median(x[x!= 0]))
                                }))),
                       .SDcols = c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes", "maternal_alcohol_useyes"),
                       by = nzero][order(nzero)] %>%
          melt(id.var = "nzero") %>%
          .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
          .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
          .[nzero ==nzero_final_cord_wa], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_Cord_ITU_wa_coef <-
  pm2_Cord_ITU_wa_coef[match(c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes", "maternal_alcohol_useyes"), pm2_Cord_ITU_wa_coef$variable),]
pm2_Cord_ITU_wa_coef$variable <- factor(pm2_Cord_ITU_wa_coef$variabl, levels=unique(pm2_Cord_ITU_wa_coef$variable))

## NOTE: median is used here instead of mean
# make frame for only significant variables:
pm2_Cord_ITU_wa_datable <- dcast(pm2_Cord_ITU_wa[,
                                               as.list(unlist(
                                                 lapply(.SD,
                                                        function(x) {
                                                          y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                                          list("non_zero" = 100 * mean(x != 0),
                                                               lcl = y[1],
                                                               ucl = y[2],
                                                               width = diff(y),
                                                               median = median(x[x!= 0]))
                                                        }))),
                                               .SDcols = c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes", "maternal_alcohol_useyes"),
                                               by = nzero][order(nzero)] %>%
                                  melt(id.var = "nzero") %>%
                                  .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
                                  .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
                                  # print %>%
                                  .[nzero == nzero_final_cord_wa & variable %in% sig_var_names_Cord_ITU_wa_finalnzero], nzero+ variable ~ metric, value.var="value")

pm2_Cord_ITU_wa_datable
```r
sig_vars_Cord_ITU_wa <-
  pm2_Cord_ITU_wa_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x=\nzero\)+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf(\%0.2f\, round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = \purple\, low = \black\, midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c(\child sex (female)\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\, \maternal alcohol use (yes)\))+
  ggplot2::labs(y=\predictor\, x = \number of non-zero coefficients = 8\, color=\%\)

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```r
coef_Cord_ITU_wa <- 
  ggplot(pm2_Cord_ITU_wa_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Cord_ITU_wa 
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/coef_Cord.png", width=2800, height=1400, res=400)
coef_Cord_ITU_wa 
dev.off()
p1 <-
  csummary_Cord_ITU_wa %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
  ggplot(pm2_Cord_ITU_wa_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  ggtitle("nzero = 7")+
  theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/Model_coef_Cord.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()

get the beta values

```r
### Code for only including \significant variables\ in the beta vector, based on VIP (>75% not-zero in bootstraps)

# get median beta values of the 1000 bootstraps for the model with 7 non-zero coefficients
Beta_hat_s_cord_wa <- matrix(miscTools::colMedians(pm2_Cord_ITU_wa[nzero == nzero_final_cord_wa, .SD, .SDcols = c(\(Intercept)\,sig_var_names_Cord_ITU_wa_finalnzero)]), ncol = 1)
# intenept and variable beta values
# NOTE that median is used here
rownames(Beta_hat_s_cord_wa) <- c(\Intercept\, sig_var_names_Cord_ITU_wa_finalnzero)

Beta_Cord_ITU_wa <- Beta_hat_s_cord_wa

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```r
```r
save(Beta_Cord_ITU_wa, file=\InputData/Data_ElasticNets/Beta_Cord_ITU_wa.Rdata\)

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[to the top](#top)


## CVS elastic net {#elasticnetCVSITU}  
main model, without alcohol variable



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```r
```r
# in case you want to start from here
load(\InputData/ClockCalculationsInput/Reg_Input_Data_CVS_ITU_EAAR_n_noNa.Rdata\)

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```r
```r
yrc_mat_ITU_CVS_n <- matrix(Reg_Input_Data_CVS_ITU_EAAR_n_noNa$EAAR_Lee)
xrc_mat_ITU_CVS_n <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_CVS_ITU_EAAR_n_noNa)[, -1]
yrc_mat_ITU_scaled_CVS_n <- scale(yrc_mat_ITU_CVS_n)
xrc_mat_ITU_scaled_CVS_n <- scale(xrc_mat_ITU_CVS_n)

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<!-- set seed -->

<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=FALSE} -->
<!-- nboot = 1000 -->

<!-- bootstraps_CVS_ITU_n <- replicate(nboot,{ -->
<!--   rws <- sample(1:nrow(xrc_mat_ITU_scaled_CVS_n), replace = TRUE); -->
<!--   ensr(xrc_mat_ITU_scaled_CVS_n[rws, ], yrc_mat_ITU_scaled_CVS_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100,nfolds=10,alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0))}, simplify = FALSE) -->

<!-- ``` -->


<!-- ```{r} -->
<!-- # save bootstrap object -->
<!-- save(bootstraps_CVS_ITU_n, file="InputData/Data_ElasticNets/bootstraps_CVS_ITU_n_1000.Rdata") -->
<!-- ``` -->



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```r
```r
load(\InputData/Data_ElasticNets/bootstraps_CVS_ITU_n_1000.Rdata\)

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```r
summaries_CVS_ITU_n <-
  bootstraps_CVS_ITU_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_CVS_ITU_n
summaries_CVS_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstraps_CVS.png", width=800, height=600)
summaries_CVS_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```r
load(\InputData/Data_ElasticNets/pm2_CVS_ITU_n.Rdata\)

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```r
csummary_CVS_ITU_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                              list(pm2_CVS_ITU_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Gestational_Age_Weeks", "Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"), by = nzero]
                                   ,
                                   pm2_CVS_ITU_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                   pm2_CVS_ITU_n[, .(median_cvm = median(cvm)), by = nzero]
                              ))[order(nzero)]

csummary_CVS_ITU_n
g1_CVS_ITU_n <-
  csummary_CVS_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

g2_CVS_ITU_n <-
  csummary_CVS_ITU_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))


gridExtra::grid.arrange(g1_CVS_ITU_n, g2_CVS_ITU_n, ncol = 1)

# note: not a big difference if mean/median cvm is used
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstrapModels_CVS.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_CVS_ITU_n, g2_CVS_ITU_n, ncol = 1)
dev.off()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_CVS.png", width=2800, height=1400, res=400)
g1_CVS_ITU_n
dev.off()
elbow_finder(csummary_CVS_ITU_n$nzero[-1], csummary_CVS_ITU_n$median_cvm[-1])
nzero_indices_CVS <- data.frame(t(elbow_finder(csummary_CVS_ITU_n$nzero[-1], csummary_CVS_ITU_n$median_cvm[-1])))
colnames(nzero_indices_CVS) <- c("x", "y")
rownames(nzero_indices_CVS) <- NULL
```r
nzero_final_CVS <- 8

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```r
nonzero_choose_CVS <- ggplot2::ggplot(csummary_CVS_ITU_n) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_CVS, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median of minimum cross-validation errors over bootstraps")+
  ggplot2::xlab("number of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[15], yend = median_cvm[15], colour = "segment"), data = csummary_CVS_ITU_n, show.legend = F)

nonzero_choose_CVS
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/nzero_choose_CVS.png", width=1600, height=1400, res=300)
nonzero_choose_CVS
dev.off()
```r
summary_CVS_ITU_n_finalnzero <- csummary_CVS_ITU_n[nzero %in% nzero_final_CVS]
sig_var_names_CVS_ITU_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_CVS_ITU_n_finalnzero[,!c(\nzero\, \mean_cvm\, \median_cvm\)]) %>% colnames()
colnames(summary_CVS_ITU_n_finalnzero) <- c(\non-zero\, \gestage at birth\, \child sex (female)\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\, \mean cvm\, \median cvm\)
summary_CVS_ITU_n_finalnzeroT <- as.data.frame(t(summary_CVS_ITU_n_finalnzero[,-c(\non-zero\, \median cvm\, \mean cvm\)]))
summary_CVS_ITU_n_finalnzeroT$variable <- rownames(summary_CVS_ITU_n_finalnzeroT)
rownames(summary_CVS_ITU_n_finalnzeroT) <- NULL
names(summary_CVS_ITU_n_finalnzeroT)[names(summary_CVS_ITU_n_finalnzeroT) == 'V1'] <- 'percent'
summary_CVS_ITU_n_finalnzeroT <- summary_CVS_ITU_n_finalnzeroT[order(summary_CVS_ITU_n_finalnzeroT$percent),]

summary_CVS_ITU_n_finalnzeroT$number <- seq(1, length(summary_CVS_ITU_n_finalnzeroT$variable))

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```r
perc_vars_CVS_ITU_n <- 
ggplot(summary_CVS_ITU_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
geom_point()+ geom_line()+
ylab("% occurence in models with nzero coefficients = 9")+
scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
xlab("variable")+
coord_flip()+
geom_hline(yintercept=0.75, linetype="dotted")+
theme_bw()

perc_vars_CVS_ITU_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_CVS_ITU_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/varsPercent_CVS.png", width=1800, height=1400, res=300)
perc_vars_CVS_ITU_n
dev.off()
```r
pm2_CVS_ITU_n_coef <-
dcast(pm2_CVS_ITU_n[,
as.list(unlist(
lapply(.SD,
function(x) {
y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
list(\non_zero\ = 100 * mean(x != 0),
lcl = y[1],
ucl = y[2],
width = diff(y),
median = median(x[x!= 0]))
}))),
.SDcols = c(\Gestational_Age_Weeks\, \Child_Sexfemale\, \Child_Birth_Weight\, \Child_Birth_Length\, \Child_Head_Circumference_At_Birth\, \Delivery_mode_dichotomaided\, \Induced_Labouryes\, \Parity_dichotomgiven birth before\, \Maternal_Age_Years\, \Maternal_Body_Mass_Index_in_Early_Pregnancy\, \Maternal_Hypertension_dichotomhypertension in current pregnancy\, \Maternal_Diabetes_dichotomdiabetes in current pregnancy\, \Maternal_Mental_DisordersYes\, \smoking_dichotomyes\),
by = nzero][order(nzero)] %>%
melt(id.var = \nzero\) %>%
.[, metric := sub(\^.+\\.(.+)$\, \\\1\, variable)] %>%
.[, variable := sub(\^(.+)\\..+$\, \\\1\, variable)] %>%
.[nzero ==nzero_final_CVS], nzero+ variable ~ metric, value.var=\value\)

# get desired order of predictors
pm2_CVS_ITU_n_coef <-
pm2_CVS_ITU_n_coef[match(c(\Gestational_Age_Weeks\, \Child_Sexfemale\, \Child_Birth_Weight\, \Child_Birth_Length\, \Child_Head_Circumference_At_Birth\, \Delivery_mode_dichotomaided\, \Induced_Labouryes\, \Parity_dichotomgiven birth before\, \Maternal_Age_Years\, \Maternal_Body_Mass_Index_in_Early_Pregnancy\, \Maternal_Hypertension_dichotomhypertension in current pregnancy\, \Maternal_Diabetes_dichotomdiabetes in current pregnancy\, \Maternal_Mental_DisordersYes\, \smoking_dichotomyes\), pm2_CVS_ITU_n_coef$variable),]
pm2_CVS_ITU_n_coef$variable <- factor(pm2_CVS_ITU_n_coef$variabl, levels=unique(pm2_CVS_ITU_n_coef$variable))

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```r
```r
write_xlsx(pm2_CVS_ITU_n_coef,\Results/Tables/CoefficientsModel_CVS.xlsx\)

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```r
```r
sig_vars_CVS_ITU_n <-
pm2_CVS_ITU_n_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x=\nzero\)+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf(\%0.2f\, round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = \purple\, low = \black\, midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c(\gestage at birth\, \child sex (female)\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\))+
  ggplot2::labs(y=\predictor\, x = \number of non-zero coefficients = 9\, color=\%\)

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```r
coef_CVS_ITU_n <- 
ggplot(pm2_CVS_ITU_n_coef, aes(y = variable, x=median))+
geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
scale_alpha(guide = 'none')+
scale_size(guide = 'none')+
geom_point()+
geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
scale_y_discrete(labels= c("gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
geom_vline(xintercept=0, linetype="dashed")+
theme_bw()+
theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_CVS_ITU_n 
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/coef_CVS.png", width=2800, height=1400, res=400)
coef_CVS_ITU_n 
dev.off()
```r
g1_CVS_ITU_n <-
  csummary_CVS_ITU_n %>%
  melt(id.vars = c(\nzero\, \mean_cvm\, \median_cvm\)) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = \purple\, low = \black\, midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c(\gestage at birth\, \child sex (female)\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y=\predictor\, x = \number of non-zero coefficients\, color=\%\)+
  ggplot2::theme(text = element_text(size = 20), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = \none\)

coef_CVS_ITU_n <- 
ggplot(pm2_CVS_ITU_n_coef, aes(y = variable, x=median))+
geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
scale_color_gradient2(high = 'green', mid = \purple\, low = \black\, midpoint =50, limits=c(0,100))+
scale_alpha(guide = 'none')+
scale_size(guide = 'none')+
geom_point()+
geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
labs(y=\\, x = \median & 95% CI of coefficient (over bootstraps)\, color=\%\)+
#ggtitle(\nzero = 8\)+
scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
scale_y_discrete(labels= c(\gestage at birth\, \child sex (female)\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\))+
geom_vline(xintercept=0, linetype=\dashed\)+
theme_bw()+
theme(text = element_text(size = 20), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
#plot.title = element_text(size=15)

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Plot:

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```r
p1 <-
  csummary_CVS_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")

p2 <- 
ggplot(pm2_CVS_ITU_n_coef, aes(y = variable, x=median))+
geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
scale_alpha(guide = 'none')+
scale_size(guide = 'none')+
geom_point()+
geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
ggtitle("nzero = 8")+
scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
scale_y_discrete(labels= c("gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
geom_vline(xintercept=0, linetype="dashed")+
theme_bw()+
theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_coef_CVS.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()

to the top

CVS elastic net

additional model, with alcohol variable

```r
# in case you want to start from here
load(\InputData/ClockCalculationsInput/Reg_Input_Data_CVS_ITU_EAAR_wa_noNa.Rdata\)

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```r
```r
yrc_mat_ITU_CVS_wa <- matrix(Reg_Input_Data_CVS_ITU_EAAR_wa_noNa$EAAR_Lee)
xrc_mat_ITU_CVS_wa <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_CVS_ITU_EAAR_wa_noNa)[, -1]
yrc_mat_ITU_scaled_CVS_wa <- scale(yrc_mat_ITU_CVS_wa)
xrc_mat_ITU_scaled_CVS_wa <- scale(xrc_mat_ITU_CVS_wa)

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<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=FALSE} -->
<!-- nboot = 1000 -->

<!-- start_time <- Sys.time() -->
<!-- bootstraps_CVS_ITU_wa <- replicate(nboot, { -->
<!--   rws <- sample(1:nrow(xrc_mat_ITU_scaled_CVS_wa), replace = TRUE) -->
<!--   ensr(xrc_mat_ITU_scaled_CVS_wa[rws, ], yrc_mat_ITU_scaled_CVS_wa[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!-- }, -->
<!-- simplify = FALSE) -->

<!-- end_time <- Sys.time() -->
<!-- end_time - start_time -->
<!-- # generates a list of length 100, each a unique call to ensr (= also a list of cv.glmnet objects, which is determined by the length of alphas) -->
<!-- # nlambda = number of lambda values, default 100 -->
<!-- # alpha: sequence of alphas to use, ensr will add length(alphas)-1 additional values (midpoints) in the construction of the alpha-lambda grid to search -->
<!-- # nfold= number of folds (default 10) for internal cv to fit hyperparameters -->

<!-- ``` -->


<!-- ```{r} -->
<!-- # save bootstrap object -->
<!-- save(bootstraps_CVS_ITU_wa, file="InputData/Data_ElasticNets/bootstraps_CVS_ITU_wa_1000.Rdata") -->
<!-- ``` -->



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```r
```r
load(\InputData/Data_ElasticNets/bootstraps_CVS_ITU_wa_1000.Rdata\)

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```r
summaries_CVS_ITU_wa <-
  bootstraps_CVS_ITU_wa %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_CVS_ITU_wa
summaries_CVS_ITU_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/bootstraps_CVS.png", width=800, height=600)
summaries_CVS_ITU_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```r
load(\InputData/Data_ElasticNets/pm2_CVS_ITU_wa.Rdata\)

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```r
csummary_CVS_ITU_wa <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                              list(pm2_CVS_ITU_wa[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Gestational_Age_Weeks", "Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"
, "maternal_alcohol_useyes"), by = nzero]
                                   ,
                                   pm2_CVS_ITU_wa[, .(mean_cvm = mean(cvm)), by = nzero],
                                   pm2_CVS_ITU_wa[, .(median_cvm = median(cvm)), by = nzero]
                              ))[order(nzero)]

csummary_CVS_ITU_wa
g1_CVS_ITU_wa <-
  csummary_CVS_ITU_wa %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("gestage at birth", "child sex", "birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking", "maternal alcohol use"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
  

g2_CVS_ITU_wa <-
  csummary_CVS_ITU_wa %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_CVS_ITU_wa, g2_CVS_ITU_wa, ncol = 1)

# note: not a big difference if mean/median cvm is used
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/Model_CVS.png", width=2800, height=1400, res=400)
g1_CVS_ITU_wa
dev.off()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/bootstrapModels_CVS.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_CVS_ITU_wa, g2_CVS_ITU_wa, ncol = 1)
dev.off()
elbow_finder(csummary_CVS_ITU_wa$nzero, csummary_CVS_ITU_wa$median_cvm)
nzero_indices_CVS <- data.frame(t(elbow_finder(csummary_CVS_ITU_wa$nzero, csummary_CVS_ITU_wa$median_cvm)))
colnames(nzero_indices_CVS) <- c("x", "y")
rownames(nzero_indices_CVS) <- NULL
nonzero_choose_CVS <- ggplot2::ggplot(csummary_CVS_ITU_wa) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_CVS, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median of minimum cross-validation errors over bootstraps")+
  ggplot2::xlab("number of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[16], yend = median_cvm[16], colour = "segment"), data = csummary_CVS_ITU_wa, show.legend = F)

nonzero_choose_CVS
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/nzero_choose_CVS.png", width=1600, height=1400, res=300)
nonzero_choose_CVS
dev.off()
```r
nzero_final_CVS_wa <- 10

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```r
csummary_CVS_ITU_wa[nzero %in% nzero_final_CVS_wa]
```r
summary_CVS_ITU_wa_finalnzero <- csummary_CVS_ITU_wa[nzero %in% nzero_final_CVS_wa]
sig_var_names_CVS_ITU_wa_finalnzero <- Filter(function(x) any(x > 0.75), summary_CVS_ITU_wa_finalnzero[,!c(\nzero\, \mean_cvm\, \median_cvm\)]) %>% colnames()
colnames(summary_CVS_ITU_wa_finalnzero) <- c(\non-zero\, \gestage at birth\, \child sex (female)\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\, \maternal alcohol (yes)\, \mean cvm\, \median cvm\)
summary_CVS_ITU_wa_finalnzeroT <- as.data.frame(t(summary_CVS_ITU_wa_finalnzero[,-c(\non-zero\, \median cvm\, \mean cvm\)]))
summary_CVS_ITU_wa_finalnzeroT$variable <- rownames(summary_CVS_ITU_wa_finalnzeroT)
rownames(summary_CVS_ITU_wa_finalnzeroT) <- NULL
names(summary_CVS_ITU_wa_finalnzeroT)[names(summary_CVS_ITU_wa_finalnzeroT) == 'V1'] <- 'percent'
summary_CVS_ITU_wa_finalnzeroT <- summary_CVS_ITU_wa_finalnzeroT[order(summary_CVS_ITU_wa_finalnzeroT$percent),]

summary_CVS_ITU_wa_finalnzeroT$number <- seq(1, length(summary_CVS_ITU_wa_finalnzeroT$variable))

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```r
perc_vars_CVS_ITU_wa <- 
ggplot(summary_CVS_ITU_wa_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
geom_point()+ geom_line()+
ylab("% occurence in models with nzero coefficients = 8")+
scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
xlab("variable")+
coord_flip()+
geom_hline(yintercept=0.75, linetype="dotted")+
theme_bw()

perc_vars_CVS_ITU_wa

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_CVS_ITU_wa_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/varsPercent_CVS.png", width=1100, height=1400, res=300)
perc_vars_CVS_ITU_wa
dev.off()
pm2_CVS_ITU_wa_coef <-
dcast(pm2_CVS_ITU_wa[,
as.list(unlist(
lapply(.SD,
function(x) {
y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
list("non_zero" = 100 * mean(x != 0),
lcl = y[1],
ucl = y[2],
width = diff(y),
median = median(x[x!= 0]))
}))),
.SDcols = c("Gestational_Age_Weeks", "Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes", "maternal_alcohol_useyes"),
by = nzero][order(nzero)] %>%
melt(id.var = "nzero") %>%
.[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
.[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
.[nzero == nzero_final_CVS_wa], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_CVS_ITU_wa_coef <-
pm2_CVS_ITU_wa_coef[match(c("Gestational_Age_Weeks", "Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes", "maternal_alcohol_useyes"), pm2_CVS_ITU_wa_coef$variable),]
pm2_CVS_ITU_wa_coef$variable <- factor(pm2_CVS_ITU_wa_coef$variabl, levels=unique(pm2_CVS_ITU_wa_coef$variable))

## NOTE: median is used here instead of mean
# make frame for only significant variables:
pm2_CVS_ITU_wa_datable <- dcast(pm2_CVS_ITU_wa[,
as.list(unlist(
lapply(.SD,
function(x) {
y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
list("non_zero" = 100 * mean(x != 0),
lcl = y[1],
ucl = y[2],
width = diff(y),
median = median(x[x!= 0]))
}))),
.SDcols = c("Gestational_Age_Weeks", "Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes", "maternal_alcohol_useyes"),
by = nzero][order(nzero)] %>%
melt(id.var = "nzero") %>%
.[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
.[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
# print %>%
.[nzero == nzero_final_CVS_wa & variable %in% sig_var_names_CVS_ITU_wa_finalnzero], nzero+ variable ~ metric, value.var="value")

pm2_CVS_ITU_wa_datable 
```r
sig_vars_CVS_ITU_wa <-
pm2_CVS_ITU_wa_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x=\nzero\)+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf(\%0.2f\, round(median, digits=2)), size=30),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = \purple\, low = \black\, midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c(\gestage at birth\, \child sex (female)\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\, \maternal alcohol use (yes)\))+
  ggplot2::labs(y=\predictor\, x = \number of non-zero coefficients = 8\, color=\%\)

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```r
coef_CVS_ITU_wa <- 
  ggplot(pm2_CVS_ITU_wa_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.5,0.4), breaks=c(-.5, -.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

coef_CVS_ITU_wa
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/coef_CVS.png", width=2800, height=1400, res=400)
coef_CVS_ITU_wa 
dev.off()
p1 <-
  g1_CVS_ITU_wa <-
  csummary_CVS_ITU_wa %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:15, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
  ggplot(pm2_CVS_ITU_wa_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  ggtitle("nzero = 10")+
  scale_x_continuous(limits=c(-0.5,0.4), breaks=c(-.5, -.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), , plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/Model_coef_CVS.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()

Placenta elastic net

main model, without alcohol variable

```r
# in case you want to start from here
load(\InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_ITU_EAAR_noNa_n.Rdata\)

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```r
```r
yrc_mat_ITU_Placenta_n <- matrix(Reg_Input_Data_Placenta_ITU_EAAR_noNa_n$EAAR_Lee)
xrc_mat_ITU_Placenta_n <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_Placenta_ITU_EAAR_noNa_n)[, -1]
yrc_mat_ITU_scaled_Placenta_n <- scale(yrc_mat_ITU_Placenta_n)
xrc_mat_ITU_scaled_Placenta_n <- scale(xrc_mat_ITU_Placenta_n)

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<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Placenta_ITU_n <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_ITU_scaled_Placenta_n), replace = TRUE) -->
<!--     ensr(xrc_mat_ITU_scaled_Placenta_n[rws, ], yrc_mat_ITU_scaled_Placenta_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!--   end_time <- Sys.time() -->
<!--   end_time - start_time -->

<!--   #Time difference of 3.159319 hours -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Placenta_ITU_n, file="InputData/Data_ElasticNets/bootstraps_Placenta_ITU_n_1000.Rdata") -->
<!-- ``` -->



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```r
```r
load(\InputData/Data_ElasticNets/bootstraps_Placenta_ITU_n_1000.Rdata\)

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```r
summaries_Placenta_ITU_n <-
  bootstraps_Placenta_ITU_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Placenta_ITU_n
summaries_Placenta_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstraps_Placenta.png", width=800, height=600)
summaries_Placenta_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```r
load(\InputData/Data_ElasticNets/pm2_Placenta_ITU_n.Rdata\)
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap

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```r
csummary_Placenta_ITU_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                                   list(pm2_Placenta_ITU_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"), by = nzero]
                                        ,
                                        pm2_Placenta_ITU_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                        pm2_Placenta_ITU_n[, .(median_cvm = median(cvm)), by = nzero]
                                   ))[order(nzero)]

csummary_Placenta_ITU_n
g1_Placenta_ITU_n <-
  csummary_Placenta_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex", "birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

g2_Placenta_ITU_n <-
  csummary_Placenta_ITU_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))



gridExtra::grid.arrange(g1_Placenta_ITU_n, g2_Placenta_ITU_n, ncol = 1)
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstrapModels_Placenta.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Placenta_ITU_n, g2_Placenta_ITU_n, ncol = 1)
dev.off()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_Placenta.png", width=2800, height=1400, res=400)
g1_Placenta_ITU_n
dev.off()
elbow_finder(csummary_Placenta_ITU_n$nzero, csummary_Placenta_ITU_n$median_cvm)

nzero_indices_Placenta <- data.frame(t(elbow_finder(csummary_Placenta_ITU_n$nzero, csummary_Placenta_ITU_n$median_cvm)))
colnames(nzero_indices_Placenta) <- c("x", "y")
rownames(nzero_indices_Placenta) <- NULL
```r
nzero_final_placenta_itu <- 7

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```r
```r
summary_Placenta_ITU_n_finalnzero <- csummary_Placenta_ITU_n[nzero %in% nzero_final_placenta_itu]
sig_var_names_Placenta_ITU_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_Placenta_ITU_n_finalnzero[,!c(\nzero\, \mean_cvm\, \median_cvm\)]) %>% colnames()
colnames(summary_Placenta_ITU_n_finalnzero) <- c(\non-zero\,\child sex (female)\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\, \mean cvm\, \median cvm\)
summary_Placenta_ITU_n_finalnzeroT <- as.data.frame(t(summary_Placenta_ITU_n_finalnzero[,-c(\non-zero\, \median cvm\, \mean cvm\)]))
summary_Placenta_ITU_n_finalnzeroT$variable <- rownames(summary_Placenta_ITU_n_finalnzeroT)
rownames(summary_Placenta_ITU_n_finalnzeroT) <- NULL
names(summary_Placenta_ITU_n_finalnzeroT)[names(summary_Placenta_ITU_n_finalnzeroT) == 'V1'] <- 'percent'
summary_Placenta_ITU_n_finalnzeroT <- summary_Placenta_ITU_n_finalnzeroT[order(summary_Placenta_ITU_n_finalnzeroT$percent),]

summary_Placenta_ITU_n_finalnzeroT$number <- seq(1, length(summary_Placenta_ITU_n_finalnzeroT$variable))

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```r
perc_vars_Placenta_ITU_n <- 
  ggplot(summary_Placenta_ITU_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("% occurence in models with nzero coefficients = 4")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("variable")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()

perc_vars_Placenta_ITU_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Placenta_ITU_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/varsPercent_Placenta.png", width=1800, height=1400, res=300)
perc_vars_Placenta_ITU_n
dev.off()
```r
pm2_Placenta_ITU_n_coef <-
  dcast(pm2_Placenta_ITU_n[,
                        as.list(unlist(
                          lapply(.SD,
                                 function(x) {
                                   y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                   list(\non_zero\ = 100 * mean(x != 0),
                                        lcl = y[1],
                                        ucl = y[2],
                                        width = diff(y),
                                        median = median(x[x!= 0]))
                                 }))),
                        .SDcols = c(\Child_Sexfemale\, \Child_Birth_Weight\, \Child_Birth_Length\, \Child_Head_Circumference_At_Birth\, \Delivery_mode_dichotomaided\, \Induced_Labouryes\, \Parity_dichotomgiven birth before\, \Maternal_Age_Years\, \Maternal_Body_Mass_Index_in_Early_Pregnancy\, \Maternal_Hypertension_dichotomhypertension in current pregnancy\, \Maternal_Diabetes_dichotomdiabetes in current pregnancy\, \Maternal_Mental_DisordersYes\, \smoking_dichotomyes\),
                        by = nzero][order(nzero)] %>%
          melt(id.var = \nzero\) %>%
          .[, metric := sub(\^.+\\.(.+)$\, \\\1\, variable)] %>%
          .[, variable := sub(\^(.+)\\..+$\, \\\1\, variable)] %>%
          .[nzero == nzero_final_placenta_itu], nzero+ variable ~ metric, value.var=\value\)

# get desired order of predictors
pm2_Placenta_ITU_n_coef <-
  pm2_Placenta_ITU_n_coef[match(c(\Child_Sexfemale\, \Child_Birth_Weight\, \Child_Birth_Length\, \Child_Head_Circumference_At_Birth\, \Delivery_mode_dichotomaided\, \Induced_Labouryes\, \Parity_dichotomgiven birth before\, \Maternal_Age_Years\, \Maternal_Body_Mass_Index_in_Early_Pregnancy\, \Maternal_Hypertension_dichotomhypertension in current pregnancy\, \Maternal_Diabetes_dichotomdiabetes in current pregnancy\, \Maternal_Mental_DisordersYes\, \smoking_dichotomyes\), pm2_Placenta_ITU_n_coef$variable),]
pm2_Placenta_ITU_n_coef$variable <- factor(pm2_Placenta_ITU_n_coef$variabl, levels=unique(pm2_Placenta_ITU_n_coef$variable))

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```r
```r
write_xlsx(pm2_Placenta_ITU_n_coef,\Results/Tables/CoefficientsModel_Placenta.xlsx\)

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```r
```r
sig_vars_Placenta_ITU_n <-
  pm2_Placenta_ITU_n_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x=\nzero\)+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf(\%0.2f\, round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = \purple\, low = \black\, midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c(\child sex (female)\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\, \maternal alcohol use (yes)\))+
  ggplot2::labs(y=\predictor\, x = \number of non-zero coefficients = 7\, color=\%\)

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```r
coef_Placenta_ITU_n <- 
  ggplot(pm2_Placenta_ITU_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Placenta_ITU_n 
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/coef_Placenta.png", width=2800, height=1400, res=400)
coef_Placenta_ITU_n
dev.off()
p1 <-
  csummary_Placenta_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size =17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
  ggplot(pm2_Placenta_ITU_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  ggtitle("nzero = 7")+
  theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_coef_Placenta.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()

to the top

Placenta elastic net

additional model, with alcohol variable

```r
# in case you want to start from here
load(\InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa.Rdata\)

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```r
```r
yrc_mat_ITU_Placenta_wa <- matrix(Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa$EAAR_Lee)
xrc_mat_ITU_Placenta_wa <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa)[, -1]
yrc_mat_ITU_scaled_Placenta_wa <- scale(yrc_mat_ITU_Placenta_wa)
xrc_mat_ITU_scaled_Placenta_wa <- scale(xrc_mat_ITU_Placenta_wa)

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<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Placenta_ITU_wa <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_ITU_scaled_Placenta_wa), replace = TRUE) -->
<!--     ensr(xrc_mat_ITU_scaled_Placenta_wa[rws, ], yrc_mat_ITU_scaled_Placenta_wa[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!--   end_time <- Sys.time() -->
<!--   end_time - start_time -->

<!--   #Time difference of 3.159319 hours -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Placenta_ITU_wa, file="InputData/Data_ElasticNets/bootstraps_Placenta_ITU_wa_1000.Rdata") -->
<!-- ``` -->




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```r
```r
load(\InputData/Data_ElasticNets/bootstraps_Placenta_ITU_wa_1000.Rdata\)

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```r
summaries_Placenta_ITU_wa <-
  bootstraps_Placenta_ITU_wa %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Placenta_ITU_wa
summaries_Placenta_ITU_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/bootstraps_Placenta.png", width=800, height=600)
summaries_Placenta_ITU_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```r
load(\InputData/Data_ElasticNets/pm2_Placenta_ITU_wa.Rdata\)
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap

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```r
csummary_Placenta_ITU_wa <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                                  list(pm2_Placenta_ITU_wa[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes", "maternal_alcohol_useyes"), by = nzero]
                                       ,
                                       pm2_Placenta_ITU_wa[, .(mean_cvm = mean(cvm)), by = nzero],
                                       pm2_Placenta_ITU_wa[, .(median_cvm = median(cvm)), by = nzero]
                                  ))[order(nzero)]

csummary_Placenta_ITU_wa
g1_Placenta_ITU_wa <-
  csummary_Placenta_ITU_wa %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex", "birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking", "maternal alcohol use"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

g2_Placenta_ITU_wa <-
  csummary_Placenta_ITU_wa %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Placenta_ITU_wa, g2_Placenta_ITU_wa, ncol = 1)
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/bootstrapModels_Placenta.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Placenta_ITU_wa, g2_Placenta_ITU_wa, ncol = 1)
dev.off()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/Model_Placenta.png", width=2800, height=1400, res=400)
g1_Placenta_ITU_wa
dev.off()
elbow_finder(csummary_Placenta_ITU_wa$nzero, csummary_Placenta_ITU_wa$median_cvm)

nzero_indices_Placenta <- data.frame(t(elbow_finder(csummary_Placenta_ITU_wa$nzero, csummary_Placenta_ITU_wa$median_cvm)))
colnames(nzero_indices_Placenta) <- c("x", "y")
rownames(nzero_indices_Placenta) <- NULL
```r
nzero_final_itu_placenta_wa <- 6

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```r
csummary_Placenta_ITU_wa[nzero %in% nzero_final_itu_placenta_wa]
nonzero_choose_Placenta <- ggplot2::ggplot(csummary_Placenta_ITU_wa) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_Placenta, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median of minimum cross-validation errors over bootstraps")+
  ggplot2::xlab("number of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[15], yend = median_cvm[15], colour = "segment"), data = csummary_Placenta_ITU_wa, show.legend = F)

nonzero_choose_Placenta
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/nzero_choose_Placenta.png", width=1600, height=1400, res=300)
nonzero_choose_Placenta
dev.off()
```r
summary_Placenta_ITU_wa_finalnzero <- csummary_Placenta_ITU_wa[nzero %in% nzero_final_itu_placenta_wa]
sig_var_names_Placenta_ITU_wa_finalnzero <- Filter(function(x) any(x > 0.75), summary_Placenta_ITU_wa_finalnzero[,!c(\nzero\, \mean_cvm\, \median_cvm\)]) %>% colnames()
colnames(summary_Placenta_ITU_wa_finalnzero) <- c(\non-zero\, \child sex (female)\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\, \maternal alcohol use (yes)\, \mean cvm\, \median cvm\)
summary_Placenta_ITU_wa_finalnzeroT <- as.data.frame(t(summary_Placenta_ITU_wa_finalnzero[,-c(\non-zero\, \median cvm\, \mean cvm\)]))
summary_Placenta_ITU_wa_finalnzeroT$variable <- rownames(summary_Placenta_ITU_wa_finalnzeroT)
rownames(summary_Placenta_ITU_wa_finalnzeroT) <- NULL
names(summary_Placenta_ITU_wa_finalnzeroT)[names(summary_Placenta_ITU_wa_finalnzeroT) == 'V1'] <- 'percent'
summary_Placenta_ITU_wa_finalzeroT <- summary_Placenta_ITU_wa_finalnzeroT[order(summary_Placenta_ITU_wa_finalnzeroT$percent),]

summary_Placenta_ITU_wa_finalnzeroT$number <- seq(1, length(summary_Placenta_ITU_wa_finalnzeroT$variable))

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```r
perc_vars_Placenta_ITU_wa <- 
  ggplot(summary_Placenta_ITU_wa_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("% occurence in models with nzero coefficients = 8")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("variable")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()

perc_vars_Placenta_ITU_wa

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Placenta_ITU_wa_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/varsPercent_Placenta.png", width=1100, height=1400, res=300)
perc_vars_Placenta_ITU_wa
dev.off()
```r
pm2_Placenta_ITU_wa_coef <-
  dcast(pm2_Placenta_ITU_wa[,
                        as.list(unlist(
                          lapply(.SD,
                                 function(x) {
                                   y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                   list(\non_zero\ = 100 * mean(x != 0),
                                        lcl = y[1],
                                        ucl = y[2],
                                        width = diff(y),
                                        median = median(x[x!= 0]))
                                 }))),
                        .SDcols = c(\Child_Sexfemale\, \Child_Birth_Weight\, \Child_Birth_Length\, \Child_Head_Circumference_At_Birth\, \Delivery_mode_dichotomaided\, \Induced_Labouryes\, \Parity_dichotomgiven birth before\, \Maternal_Age_Years\, \Maternal_Body_Mass_Index_in_Early_Pregnancy\, \Maternal_Hypertension_dichotomhypertension in current pregnancy\, \Maternal_Diabetes_dichotomdiabetes in current pregnancy\, \Maternal_Mental_DisordersYes\, \smoking_dichotomyes\, \maternal_alcohol_useyes\),
                        by = nzero][order(nzero)] %>%
          melt(id.var = \nzero\) %>%
          .[, metric := sub(\^.+\\.(.+)$\, \\\1\, variable)] %>%
          .[, variable := sub(\^(.+)\\..+$\, \\\1\, variable)] %>%
          .[nzero == nzero_final_itu_placenta_wa], nzero+ variable ~ metric, value.var=\value\)

# get desired order of predictors
pm2_Placenta_ITU_wa_coef <-
  pm2_Placenta_ITU_wa_coef[match(c(\Child_Sexfemale\, \Child_Birth_Weight\, \Child_Birth_Length\, \Child_Head_Circumference_At_Birth\, \Delivery_mode_dichotomaided\, \Induced_Labouryes\, \Parity_dichotomgiven birth before\, \Maternal_Age_Years\, \Maternal_Body_Mass_Index_in_Early_Pregnancy\, \Maternal_Hypertension_dichotomhypertension in current pregnancy\, \Maternal_Diabetes_dichotomdiabetes in current pregnancy\, \Maternal_Mental_DisordersYes\, \smoking_dichotomyes\, \maternal_alcohol_useyes\), pm2_Placenta_ITU_wa_coef$variable),]
pm2_Placenta_ITU_wa_coef$variable <- factor(pm2_Placenta_ITU_wa_coef$variabl, levels=unique(pm2_Placenta_ITU_wa_coef$variable))

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```r
```r
sig_vars_Placenta_ITU_wa <-
  pm2_Placenta_ITU_wa_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x=\nzero\)+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf(\%0.2f\, round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = \purple\, low = \black\, midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c(\child sex (female)\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\, \maternal alcohol use (yes)\))+
  ggplot2::labs(y=\predictor\, x = \number of non-zero coefficients = 7\, color=\%\)

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```r
coef_Placenta_ITU_wa <- 
  ggplot(pm2_Placenta_ITU_wa_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Placenta_ITU_wa 
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/coef_Placenta.png", width=2800, height=1400, res=400)
coef_Placenta_ITU_wa
dev.off()
p1 <-
   csummary_Placenta_ITU_wa %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
  ggplot(pm2_Placenta_ITU_wa_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  ggtitle("nzero = 6")+
  theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/Model_coef_Placenta.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()

to the top

Placenta elastic net splitted by sex

model without alcohol variable, but splitted by sex

males

```r
# in case you want to start from here
load(\InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_n.Rdata\)
Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_n$Child_Sex <- NULL

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```r
```r
yrc_mat_ITU_Placenta_male_n <- matrix(Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_n$EAAR_Lee)
xrc_mat_ITU_Placenta_male_n <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_n)[, -1]
yrc_mat_ITU_scaled_Placenta_male_n <- scale(yrc_mat_ITU_Placenta_male_n)
xrc_mat_ITU_scaled_Placenta_male_n <- scale(xrc_mat_ITU_Placenta_male_n)

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<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Placenta_male_ITU_n <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_ITU_scaled_Placenta_male_n), replace = TRUE) -->
<!--     ensr(xrc_mat_ITU_scaled_Placenta_male_n[rws, ], yrc_mat_ITU_scaled_Placenta_male_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Placenta_male_ITU_n, file="InputData/Data_ElasticNets/bootstraps_Placenta_male_ITU_n_1000.Rdata") -->
<!-- ``` -->



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```r
```r
load(\InputData/Data_ElasticNets/bootstraps_Placenta_male_ITU_n_1000.Rdata\)

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```r
summaries_Placenta_male_ITU_n <-
  bootstraps_Placenta_male_ITU_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Placenta_male_ITU_n
summaries_Placenta_male_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
```r
png(filename=\Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/bootstraps_Placenta_MALE.png\, width=800, height=600)
summaries_Placenta_male_ITU_n[, .SD[cvm == min(cvm)], by = c(\bootstrap\, \nzero\)] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()

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<!-- ```{r, warning=FALSE} -->
<!-- # lowest cvm by bootstrap and nzero -->
<!-- pm_Placenta_male_ITU_n <- summaries_Placenta_male_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] -->
<!-- pm2_Placenta_male_ITU_n <- NULL -->

<!-- for(i in as.integer(seq(1, nrow(pm_Placenta_male_ITU_n), by = 1))) { -->
<!--   pm2_Placenta_male_ITU_n <- rbind(pm2_Placenta_male_ITU_n, -->
<!--                cbind(pm_Placenta_male_ITU_n[i, ], -->
<!--                t(as.matrix(coef(bootstraps_Placenta_male_ITU_n[[pm_Placenta_male_ITU_n[i, bootstrap]]][[pm_Placenta_male_ITU_n[i, l_index]]], s = pm_Placenta_male_ITU_n[i, lambda]))) -->
<!--                ) -->
<!--   ) -->
<!-- } -->

<!-- pm2_Placenta_male_ITU_n -->
<!-- ``` -->


<!-- ```{r} -->
<!-- # save "preferable models" -->
<!-- save(pm2_Placenta_male_ITU_n, file="InputData/Data_ElasticNets/pm2_Placenta_male_ITU_n.Rdata") -->
<!-- ``` -->



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```r
```r
load(\InputData/Data_ElasticNets/pm2_Placenta_male_ITU_n.Rdata\)
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap

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```r
csummary_Placenta_male_ITU_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                              list(pm2_Placenta_male_ITU_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"), by = nzero]
                                   ,
                                   pm2_Placenta_male_ITU_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                   pm2_Placenta_male_ITU_n[, .(median_cvm = median(cvm)), by = nzero]
                              ))[order(nzero)]

csummary_Placenta_male_ITU_n
g1_Placenta_male_ITU_n <-
  csummary_Placenta_male_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

g2_Placenta_male_ITU_n <-
  csummary_Placenta_male_ITU_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))


gridExtra::grid.arrange(g1_Placenta_male_ITU_n, g2_Placenta_male_ITU_n, ncol = 1)
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/bootstrapModels_Placenta_male.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Placenta_male_ITU_n, g2_Placenta_male_ITU_n, ncol = 1)
dev.off()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/Model_Placenta_male.png", width=2800, height=1400, res=400)
g1_Placenta_male_ITU_n
dev.off()
elbow_finder(csummary_Placenta_male_ITU_n$nzero[-13], csummary_Placenta_male_ITU_n$median_cvm[-13])

nzero_indices_Cord <- data.frame(t(elbow_finder(csummary_Placenta_male_ITU_n$nzero[-13], csummary_Placenta_male_ITU_n$median_cvm[-13])))
colnames(nzero_indices_Cord) <- c("x", "y")
rownames(nzero_indices_Cord) <- NULL
```r
nzero_final_placenta_male <- 5

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```r
csummary_Placenta_male_ITU_n[nzero %in% nzero_final_placenta_male]
```r
summary_Placenta_male_ITU_n_finalnzero <- csummary_Placenta_male_ITU_n[nzero %in% nzero_final_placenta_male]
sig_var_names_Placenta_male_ITU_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_Placenta_male_ITU_n_finalnzero[,!c(\nzero\, \mean_cvm\, \median_cvm\)]) %>% colnames()
colnames(summary_Placenta_male_ITU_n_finalnzero) <- c(\non-zero\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\, \mean cvm\, \median cvm\)
summary_Placenta_male_ITU_n_finalnzeroT <- as.data.frame(t(summary_Placenta_male_ITU_n_finalnzero[,-c(\non-zero\, \median cvm\, \mean cvm\)]))
summary_Placenta_male_ITU_n_finalnzeroT$variable <- rownames(summary_Placenta_male_ITU_n_finalnzeroT)
rownames(summary_Placenta_male_ITU_n_finalnzeroT) <- NULL
names(summary_Placenta_male_ITU_n_finalnzeroT)[names(summary_Placenta_male_ITU_n_finalnzeroT) == 'V1'] <- 'percent'
summary_Placenta_male_ITU_n_finalnzeroT <- summary_Placenta_male_ITU_n_finalnzeroT[order(summary_Placenta_male_ITU_n_finalnzeroT$percent),]

summary_Placenta_male_ITU_n_finalnzeroT$number <- seq(1, length(summary_Placenta_male_ITU_n_finalnzeroT$variable))

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```r
perc_vars_Placenta_male_ITU_n <- 
  ggplot(summary_Placenta_male_ITU_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("% occurence in models with nzero coefficients = 2")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("variable")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()

perc_vars_Placenta_male_ITU_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Placenta_male_ITU_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/varsPercent_Placenta_male.png", width=1100, height=1400, res=300)
perc_vars_Placenta_male_ITU_n
dev.off()
```r
pm2_Placenta_male_ITU_n_coef <-
  dcast(pm2_Placenta_male_ITU_n[,
                       as.list(unlist(
                         lapply(.SD,
                                function(x) {
                                  y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                  list(\non_zero\ = 100 * mean(x != 0),
                                       lcl = y[1],
                                       ucl = y[2],
                                       width = diff(y),
                                       median = median(x[x!= 0]))
                                }))),
                       .SDcols = c(\Child_Birth_Weight\, \Child_Birth_Length\, \Child_Head_Circumference_At_Birth\, \Delivery_mode_dichotomaided\, \Induced_Labouryes\, \Parity_dichotomgiven birth before\, \Maternal_Age_Years\, \Maternal_Body_Mass_Index_in_Early_Pregnancy\, \Maternal_Hypertension_dichotomhypertension in current pregnancy\, \Maternal_Diabetes_dichotomdiabetes in current pregnancy\, \Maternal_Mental_DisordersYes\, \smoking_dichotomyes\),
                       by = nzero][order(nzero)] %>%
          melt(id.var = \nzero\) %>%
          .[, metric := sub(\^.+\\.(.+)$\, \\\1\, variable)] %>%
          .[, variable := sub(\^(.+)\\..+$\, \\\1\, variable)] %>%
          .[nzero ==nzero_final_placenta_male], nzero+ variable ~ metric, value.var=\value\)

# get desired order of predictors
pm2_Placenta_male_ITU_n_coef <-
  pm2_Placenta_male_ITU_n_coef[match(c(\Child_Birth_Weight\, \Child_Birth_Length\, \Child_Head_Circumference_At_Birth\, \Delivery_mode_dichotomaided\, \Induced_Labouryes\, \Parity_dichotomgiven birth before\, \Maternal_Age_Years\, \Maternal_Body_Mass_Index_in_Early_Pregnancy\, \Maternal_Hypertension_dichotomhypertension in current pregnancy\, \Maternal_Diabetes_dichotomdiabetes in current pregnancy\, \Maternal_Mental_DisordersYes\, \smoking_dichotomyes\), pm2_Placenta_male_ITU_n_coef$variable),]
pm2_Placenta_male_ITU_n_coef$variable <- factor(pm2_Placenta_male_ITU_n_coef$variabl, levels=unique(pm2_Placenta_male_ITU_n_coef$variable))

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```r
```r
sig_vars_Placenta_male_ITU_n <-
  pm2_Placenta_male_ITU_n_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x=\nzero\)+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf(\%0.2f\, round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = \purple\, low = \black\, midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c(\birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\))+
  ggplot2::labs(y=\predictor\, x = \number of non-zero coefficients = 2\, color=\%\)

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```r
coef_Placenta_male_ITU_n <- 
  ggplot(pm2_Placenta_male_ITU_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Placenta_male_ITU_n
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/coef_Placenta_male.png", width=2800, height=1400, res=400)
coef_Placenta_male_ITU_n
dev.off()
p1 <-
  csummary_Placenta_male_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
  ggplot(pm2_Placenta_male_ITU_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  ggtitle("nzero = 5")+
  theme(text = element_text(size =17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/Model_coef_Placenta_male.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()

to the top

females

```r
# in case you want to start from here
load(\InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_n.Rdata\)
Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_n$Child_Sex <- NULL

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```r
```r
yrc_mat_ITU_Placenta_female_n <- matrix(Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_n$EAAR_Lee)
xrc_mat_ITU_Placenta_female_n <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_n)[, -1]
yrc_mat_ITU_scaled_Placenta_female_n <- scale(yrc_mat_ITU_Placenta_female_n)
xrc_mat_ITU_scaled_Placenta_female_n <- scale(xrc_mat_ITU_Placenta_female_n)

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<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Placenta_female_ITU_n <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_ITU_scaled_Placenta_female_n), replace = TRUE) -->
<!--     ensr(xrc_mat_ITU_scaled_Placenta_female_n[rws, ], yrc_mat_ITU_scaled_Placenta_female_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!--   end_time <- Sys.time() -->
<!--   end_time - start_time -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Placenta_female_ITU_n, file="InputData/Data_ElasticNets/bootstraps_Placenta_female_ITU_n_1000.Rdata") -->
<!-- ``` -->



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```r
```r
load(\InputData/Data_ElasticNets/bootstraps_Placenta_female_ITU_n_1000.Rdata\)

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```r
summaries_Placenta_female_ITU_n <-
  bootstraps_Placenta_female_ITU_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Placenta_female_ITU_n
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/bootstraps_Placenta_FEMALE.png", width=800, height=600)
summaries_Placenta_female_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```r
load(\InputData/Data_ElasticNets/pm2_Placenta_female_ITU_n.Rdata\)
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap

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```r
csummary_Placenta_female_ITU_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                                       list(pm2_Placenta_female_ITU_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"), by = nzero]
                                            ,
                                            pm2_Placenta_female_ITU_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                            pm2_Placenta_female_ITU_n[, .(median_cvm = median(cvm)), by = nzero]
                                       ))[order(nzero)]

csummary_Placenta_female_ITU_n
g1_Placenta_female_ITU_n <-
  csummary_Placenta_female_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
  

g2_Placenta_female_ITU_n <-
  csummary_Placenta_female_ITU_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Placenta_female_ITU_n, g2_Placenta_female_ITU_n, ncol = 1)
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/Model_Placenta_female.png", width=2800, height=1400, res=400)
g1_Placenta_female_ITU_n
dev.off()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/bootstrapModels_Placenta_female.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Placenta_female_ITU_n, g2_Placenta_female_ITU_n, ncol = 1)
dev.off()
elbow_finder(csummary_Placenta_female_ITU_n$nzero, csummary_Placenta_female_ITU_n$median_cvm)

nzero_indices_Cord <- data.frame(t(elbow_finder(csummary_Placenta_female_ITU_n$nzero, csummary_Placenta_female_ITU_n$median_cvm)))
colnames(nzero_indices_Cord) <- c("x", "y")
rownames(nzero_indices_Cord) <- NULL
```r
nzero_final_placenta_female <- 7

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```r
csummary_Placenta_female_ITU_n[nzero %in% nzero_final_placenta_female]
nonzero_choose_Placenta_female <- ggplot2::ggplot(csummary_Placenta_female_ITU_n) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_Cord, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median of minimum cross-validation errors over bootstraps")+
  ggplot2::xlab("number of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[13], yend = median_cvm[13], colour = "segment"), data = csummary_Placenta_female_ITU_n, show.legend = F)

nonzero_choose_Placenta_female
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/nzero_choose_Placenta_female.png", width=1600, height=1400, res=300)
nonzero_choose_Placenta_female
dev.off()
```r
summary_Placenta_female_ITU_n_finalnzero <- csummary_Placenta_female_ITU_n[nzero %in% nzero_final_placenta_female]
sig_var_names_Placenta_female_ITU_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_Placenta_female_ITU_n_finalnzero[,!c(\nzero\, \mean_cvm\, \median_cvm\)]) %>% colnames()
colnames(summary_Placenta_female_ITU_n_finalnzero) <- c(\non-zero\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\, \mean cvm\, \median cvm\)
summary_Placenta_female_ITU_n_finalnzeroT <- as.data.frame(t(summary_Placenta_female_ITU_n_finalnzero[,-c(\non-zero\, \median cvm\, \mean cvm\)]))
summary_Placenta_female_ITU_n_finalnzeroT$variable <- rownames(summary_Placenta_female_ITU_n_finalnzeroT)
rownames(summary_Placenta_female_ITU_n_finalnzeroT) <- NULL
names(summary_Placenta_female_ITU_n_finalnzeroT)[names(summary_Placenta_female_ITU_n_finalnzeroT) == 'V1'] <- 'percent'
summary_Placenta_female_ITU_n_finalnzeroT <- summary_Placenta_female_ITU_n_finalnzeroT[order(summary_Placenta_female_ITU_n_finalnzeroT$percent),]

summary_Placenta_female_ITU_n_finalnzeroT$number <- seq(1, length(summary_Placenta_female_ITU_n_finalnzeroT$variable))

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```r
perc_vars_Placenta_female_ITU_n <- 
  ggplot(summary_Placenta_female_ITU_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("% occurence in models with nzero coefficients = 7")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("variable")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()

perc_vars_Placenta_female_ITU_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Placenta_female_ITU_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/varsPercent_Placenta_female.png", width=1100, height=1400, res=300)
perc_vars_Placenta_female_ITU_n
dev.off()
```r
pm2_Placenta_female_ITU_n_coef <-
  dcast(pm2_Placenta_female_ITU_n[,
                                as.list(unlist(
                                  lapply(.SD,
                                         function(x) {
                                           y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                           list(\non_zero\ = 100 * mean(x != 0),
                                                lcl = y[1],
                                                ucl = y[2],
                                                width = diff(y),
                                                median = median(x[x!= 0]))
                                         }))),
                                .SDcols = c(\Child_Birth_Weight\, \Child_Birth_Length\, \Child_Head_Circumference_At_Birth\, \Delivery_mode_dichotomaided\, \Induced_Labouryes\, \Parity_dichotomgiven birth before\, \Maternal_Age_Years\, \Maternal_Body_Mass_Index_in_Early_Pregnancy\, \Maternal_Hypertension_dichotomhypertension in current pregnancy\, \Maternal_Diabetes_dichotomdiabetes in current pregnancy\, \Maternal_Mental_DisordersYes\, \smoking_dichotomyes\),
                                by = nzero][order(nzero)] %>%
          melt(id.var = \nzero\) %>%
          .[, metric := sub(\^.+\\.(.+)$\, \\\1\, variable)] %>%
          .[, variable := sub(\^(.+)\\..+$\, \\\1\, variable)] %>%
          .[nzero ==nzero_final_placenta_female], nzero+ variable ~ metric, value.var=\value\)

# get desired order of predictors
pm2_Placenta_female_ITU_n_coef <-
  pm2_Placenta_female_ITU_n_coef[match(c(\Child_Birth_Weight\, \Child_Birth_Length\, \Child_Head_Circumference_At_Birth\, \Delivery_mode_dichotomaided\, \Induced_Labouryes\, \Parity_dichotomgiven birth before\, \Maternal_Age_Years\, \Maternal_Body_Mass_Index_in_Early_Pregnancy\, \Maternal_Hypertension_dichotomhypertension in current pregnancy\, \Maternal_Diabetes_dichotomdiabetes in current pregnancy\, \Maternal_Mental_DisordersYes\, \smoking_dichotomyes\), pm2_Placenta_female_ITU_n_coef$variable),]
pm2_Placenta_female_ITU_n_coef$variable <- factor(pm2_Placenta_female_ITU_n_coef$variabl, levels=unique(pm2_Placenta_female_ITU_n_coef$variable))

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```r
```r
sig_vars_Placenta_female_ITU_n <-
  pm2_Placenta_female_ITU_n_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x=\nzero\)+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf(\%0.2f\, round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = \purple\, low = \black\, midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c(\birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\))+
  ggplot2::labs(y=\predictor\, x = \number of non-zero coefficients = 4\, color=\%\)

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```r
coef_Placenta_female_ITU_n <- 
  ggplot(pm2_Placenta_female_ITU_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Placenta_female_ITU_n
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/coef_Placenta_female.png",  width=2800, height=1400, res=400)
coef_Placenta_female_ITU_n
dev.off()
p1 <-
  csummary_Placenta_female_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
coef_Placenta_female_ITU_n <- 
  ggplot(pm2_Placenta_female_ITU_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  ggtitle("nzero = 7")+
  theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/Model_coef_Placenta_female.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()

to the top

PREDO

Placenta elastic net

```r
# in case you want to start from here
load(\InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n.Rdata\)

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```r
```r
yrc_mat_PREDO_Placenta_n <- matrix(Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n$EAAR_Lee)
xrc_mat_PREDO_Placenta_n <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n)[, -1]
yrc_mat_PREDO_scaled_Placenta_n <- scale(yrc_mat_PREDO_Placenta_n)
xrc_mat_PREDO_scaled_Placenta_n <- scale(xrc_mat_PREDO_Placenta_n)

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<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Placenta_PREDO_n <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_PREDO_scaled_Placenta_n), replace = TRUE) -->
<!--     ensr(xrc_mat_PREDO_scaled_Placenta_n[rws, ], yrc_mat_PREDO_scaled_Placenta_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!--   end_time <- Sys.time() -->
<!--   end_time - start_time -->

<!--   #Time difference of 3.159319 hours -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Placenta_PREDO_n, file="InputData/Data_ElasticNets/bootstraps_Placenta_PREDO_n_1000.Rdata") -->
<!-- ``` -->



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```r
```r
load(\InputData/Data_ElasticNets/bootstraps_Placenta_PREDO_n_1000.Rdata\)

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```r
summaries_Placenta_PREDO_n <-
  bootstraps_Placenta_PREDO_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Placenta_PREDO_n
summaries_Placenta_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstraps_Placenta_PREDO.png", width=800, height=600)
summaries_Placenta_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```r
load(\InputData/Data_ElasticNets/pm2_Placenta_PREDO_n.Rdata\)
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap

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```r
csummary_Placenta_PREDO_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                                     list(pm2_Placenta_PREDO_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"), by = nzero]
                                          ,
                                          pm2_Placenta_PREDO_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                          pm2_Placenta_PREDO_n[, .(median_cvm = median(cvm)), by = nzero]
                                     ))[order(nzero)]

csummary_Placenta_PREDO_n
g1_Placenta_PREDO_n <-
  csummary_Placenta_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex", "birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

g2_Placenta_PREDO_n <-
  csummary_Placenta_PREDO_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Placenta_PREDO_n, g2_Placenta_PREDO_n, ncol = 1)
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstrapModels_Placenta_PREDO.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Placenta_PREDO_n, g2_Placenta_PREDO_n, ncol = 1)
dev.off()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_Placenta_PREDO.png", width=2800, height=1400, res=400)
g1_Placenta_PREDO_n
dev.off()
elbow_finder(csummary_Placenta_PREDO_n$nzero, csummary_Placenta_PREDO_n$median_cvm)

nzero_indices_Placenta_PREDO <- data.frame(t(elbow_finder(csummary_Placenta_PREDO_n$nzero, csummary_Placenta_PREDO_n$median_cvm)))
colnames(nzero_indices_Placenta_PREDO) <- c("x", "y")
rownames(nzero_indices_Placenta_PREDO) <- NULL
```r
nzero_final_placenta_predo <- 6

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```r
csummary_Placenta_PREDO_n[nzero %in% nzero_final_placenta_predo]
nonzero_choose_Placenta_PREDO <- ggplot2::ggplot(csummary_Placenta_PREDO_n) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_Placenta_PREDO, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median of minimum cross-validation errors over bootstraps")+
  ggplot2::xlab("number of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[14], yend = median_cvm[14], colour = "segment"), data = csummary_Placenta_PREDO_n, show.legend = F)

nonzero_choose_Placenta_PREDO
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/nzero_choose_Placenta_PREDO.png", width=1600, height=1400, res=300)
nonzero_choose_Placenta_PREDO
dev.off()
```r
summary_Placenta_PREDO_n_finalnzero <- csummary_Placenta_PREDO_n[nzero %in% nzero_final_placenta_predo]
sig_var_names_Placenta_PREDO_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_Placenta_PREDO_n_finalnzero[,!c(\nzero\, \mean_cvm\, \median_cvm\)]) %>% colnames()
colnames(summary_Placenta_PREDO_n_finalnzero) <- c(\non-zero\,\child sex\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\, \mean cvm\, \median cvm\)
summary_Placenta_PREDO_n_finalnzeroT <- as.data.frame(t(summary_Placenta_PREDO_n_finalnzero[,-c(\non-zero\, \median cvm\, \mean cvm\)]))
summary_Placenta_PREDO_n_finalnzeroT$variable <- rownames(summary_Placenta_PREDO_n_finalnzeroT)
rownames(summary_Placenta_PREDO_n_finalnzeroT) <- NULL
names(summary_Placenta_PREDO_n_finalnzeroT)[names(summary_Placenta_PREDO_n_finalnzeroT) == 'V1'] <- 'percent'
summary_Placenta_PREDO_n_finalnzeroT <- summary_Placenta_PREDO_n_finalnzeroT[order(summary_Placenta_PREDO_n_finalnzeroT$percent),]

summary_Placenta_PREDO_n_finalnzeroT$number <- seq(1, length(summary_Placenta_PREDO_n_finalnzeroT$variable))

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```r
perc_vars_Placenta_PREDO_n <- 
  ggplot(summary_Placenta_PREDO_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("% occurence in models with nzero coefficients = 5")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("variable")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()

perc_vars_Placenta_PREDO_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Placenta_PREDO_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/varsPercent_Placenta_PREDO.png", width=1100, height=1400, res=400)
perc_vars_Placenta_PREDO_n
dev.off()
pm2_Placenta_PREDO_n_coef <-
  dcast(pm2_Placenta_PREDO_n[,
                                as.list(unlist(
                                  lapply(.SD,
                                         function(x) {
                                           y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                           list("non_zero" = 100 * mean(x != 0),
                                                lcl = y[1],
                                                ucl = y[2],
                                                width = diff(y),
                                                median = median(x[x!= 0]))
                                         }))),
                                .SDcols = c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"),
                                by = nzero][order(nzero)] %>%
          melt(id.var = "nzero") %>%
          .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
          .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
          .[nzero ==nzero_final_placenta_predo], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_Placenta_PREDO_n_coef <-
  pm2_Placenta_PREDO_n_coef[match(c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"), pm2_Placenta_PREDO_n_coef$variable),]
pm2_Placenta_PREDO_n_coef$variable <- factor(pm2_Placenta_PREDO_n_coef$variabl, levels=unique(pm2_Placenta_PREDO_n_coef$variable))

## NOTE: median is used here instead of mean
# make frame for only significant variables:
pm2_Placenta_PREDO_n_datable <- dcast(pm2_Placenta_PREDO_n[,
                                                                 as.list(unlist(
                                                                   lapply(.SD,
                                                                          function(x) {
                                                                            y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                                                            list("non_zero" = 100 * mean(x != 0),
                                                                                 lcl = y[1],
                                                                                 ucl = y[2],
                                                                                 width = diff(y),
                                                                                 median = median(x[x!= 0]))
                                                                          }))),
                                                                 .SDcols = c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"),
                                                                 by = nzero][order(nzero)] %>%
                                           melt(id.var = "nzero") %>%
                                           .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
                                           .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
                                           # print %>%
                                           .[nzero == nzero_final_placenta_predo & variable %in% sig_var_names_Placenta_PREDO_n_finalnzero], nzero+ variable ~ metric, value.var="value")
pm2_Placenta_PREDO_n_coef
```r
write_xlsx(pm2_Placenta_PREDO_n_coef,\Results/Tables/CoefficientsModel_Placenta_PREDO.xlsx\)

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```r
```r
sig_vars_Placenta_PREDO_n <-
  pm2_Placenta_PREDO_n_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x=\nzero\)+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf(\%0.2f\, round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = \purple\, low = \black\, midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c(\child sex\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\))+
  ggplot2::labs(y=\predictor\, x = \number of non-zero coefficients = 5\, color=\%\)

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```r
coef_Placenta_PREDO_n <- 
  ggplot(pm2_Placenta_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.5,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Placenta_PREDO_n
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/coef_Placenta_PREDO.png", width=2800, height=1400, res=400)
coef_Placenta_PREDO_n
dev.off()
p1 <-
  csummary_Placenta_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
  ggplot(pm2_Placenta_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.5,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  ggtitle("nzero = 6")+
  theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_coef_Placenta_PREDO.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()

to the top

Placenta elastic net

```r
# in case you want to start from here
load(\InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa.Rdata\)

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```r
```r
yrc_mat_PREDO_Placenta_wa <- matrix(Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa$EAAR_Lee)
xrc_mat_PREDO_Placenta_wa <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa)[, -1]
yrc_mat_PREDO_scaled_Placenta_wa <- scale(yrc_mat_PREDO_Placenta_wa)
xrc_mat_PREDO_scaled_Placenta_wa <- scale(xrc_mat_PREDO_Placenta_wa)

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<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Placenta_PREDO_wa <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_PREDO_scaled_Placenta_wa), replace = TRUE) -->
<!--     ensr(xrc_mat_PREDO_scaled_Placenta_wa[rws, ], yrc_mat_PREDO_scaled_Placenta_wa[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!--   end_time <- Sys.time() -->
<!--   end_time - start_time -->

<!--   #Time difference of 3.159319 hours -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Placenta_PREDO_wa, file="InputData/Data_ElasticNets/bootstraps_Placenta_PREDO_wa_1000.Rdata") -->
<!-- ``` -->



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```r
```r
load(\InputData/Data_ElasticNets/bootstraps_Placenta_PREDO_wa_1000.Rdata\)

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```r
summaries_Placenta_PREDO_wa <-
  bootstraps_Placenta_PREDO_wa %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Placenta_PREDO_wa
summaries_Placenta_PREDO_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
```r
png(filename=\Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/bootstraps_Placenta_PREDO.png\, width=800, height=600)
summaries_Placenta_PREDO_wa[, .SD[cvm == min(cvm)], by = c(\bootstrap\, \nzero\)] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()

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<!-- ```{r, warning=FALSE} -->
<!-- # lowest cvm by bootstrap and nzero -->
<!-- pm_Placenta_PREDO_wa <- summaries_Placenta_PREDO_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] -->
<!-- pm2_Placenta_PREDO_wa <- NULL -->

<!-- for(i in as.integer(seq(1, nrow(pm_Placenta_PREDO_wa), by = 1))) { -->
<!--   pm2_Placenta_PREDO_wa <- rbind(pm2_Placenta_PREDO_wa, -->
<!--                cbind(pm_Placenta_PREDO_wa[i, ], -->
<!--                t(as.matrix(coef(bootstraps_Placenta_PREDO_wa[[pm_Placenta_PREDO_wa[i, bootstrap]]][[pm_Placenta_PREDO_wa[i, l_index]]], s = pm_Placenta_PREDO_wa[i, lambda]))) -->
<!--                ) -->
<!--   ) -->
<!-- } -->

<!-- pm2_Placenta_PREDO_wa -->
<!-- ``` -->


<!-- ```{r} -->
<!-- # save "preferable models" -->
<!-- save(pm2_Placenta_PREDO_wa, file="InputData/Data_ElasticNets/pm2_Placenta_PREDO_wa.Rdata") -->
<!-- ``` -->



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```r
```r
load(\InputData/Data_ElasticNets/pm2_Placenta_PREDO_wa.Rdata\)
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap

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look how often a particular variable is associated with a non-zero coefficient in a model with a given number of non-zero coefficients (over all bootstraps)


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```r
csummary_Placenta_PREDO_wa <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                                       list(pm2_Placenta_PREDO_wa[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes", "Alcohol_Use_In_Early_Pregnancy_19Octyes"), by = nzero]
                                            ,
                                            pm2_Placenta_PREDO_wa[, .(mean_cvm = mean(cvm)), by = nzero],
                                            pm2_Placenta_PREDO_wa[, .(median_cvm = median(cvm)), by = nzero]
                                       ))[order(nzero)]

csummary_Placenta_PREDO_wa
g1_Placenta_PREDO_wa <-
  csummary_Placenta_PREDO_wa %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex","birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking", "maternal alcohol use"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

g2_Placenta_PREDO_wa <-
  csummary_Placenta_PREDO_wa %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Placenta_PREDO_wa, g2_Placenta_PREDO_wa, ncol = 1)
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstrapModels_Placenta_PREDO.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Placenta_PREDO_wa, g2_Placenta_PREDO_wa, ncol = 1)
dev.off()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_Placena_PREDO.png", width=2800, height=1400, res=400)
g1_Placenta_PREDO_wa
dev.off()
elbow_finder(csummary_Placenta_PREDO_wa$nzero, csummary_Placenta_PREDO_wa$median_cvm)

nzero_indices_Placenta_PREDO_wa<- data.frame(t(elbow_finder(csummary_Placenta_PREDO_wa$nzero, csummary_Placenta_PREDO_wa$median_cvm)))
colnames(nzero_indices_Placenta_PREDO_wa) <- c("x", "y")
rownames(nzero_indices_Placenta_PREDO_wa) <- NULL

look at models with 7 non-zero coefficient.

nzero_final_placenta_predo_wa <- 9
csummary_Placenta_PREDO_wa[nzero %in% nzero_final_placenta_predo_wa]
nonzero_choose_Placenta_PREDO_wa <- ggplot2::ggplot(csummary_Placenta_PREDO_wa) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_Placenta_PREDO_wa, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median of minimum cross-validation errors over bootstraps")+
  ggplot2::xlab("number of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[14], yend = median_cvm[14], colour = "segment"), data = csummary_Placenta_PREDO_wa, show.legend = F)

nonzero_choose_Placenta_PREDO_wa
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/nzero_choose_Placenta_PREDO.png", width=1600, height=1400, res=300)
nonzero_choose_Placenta_PREDO_wa
dev.off()
summary_Placenta_PREDO_wa_finalnzero <- csummary_Placenta_PREDO_wa[nzero %in% nzero_final_placenta_predo_wa]
sig_var_names_Placenta_PREDO_wa_finalnzero <- Filter(function(x) any(x > 0.75), summary_Placenta_PREDO_wa_finalnzero[,!c("nzero", "mean_cvm", "median_cvm")]) %>% colnames()
colnames(summary_Placenta_PREDO_wa_finalnzero) <- c("non-zero", "child sex", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal \alcohol use (yes)", "mean cvm", "median cvm")
summary_Placenta_PREDO_wa_finalnzeroT <- as.data.frame(t(summary_Placenta_PREDO_wa_finalnzero[,-c("non-zero", "median cvm", "mean cvm")]))
summary_Placenta_PREDO_wa_finalnzeroT$variable <- rownames(summary_Placenta_PREDO_wa_finalnzeroT)
rownames(summary_Placenta_PREDO_wa_finalnzeroT) <- NULL
names(summary_Placenta_PREDO_wa_finalnzeroT)[names(summary_Placenta_PREDO_wa_finalnzeroT) == 'V1'] <- 'percent'
summary_Placenta_PREDO_wa_finalnzeroT <- summary_Placenta_PREDO_wa_finalnzeroT[order(summary_Placenta_PREDO_wa_finalnzeroT$percent),]

summary_Placenta_PREDO_wa_finalnzeroT$number <- seq(1, length(summary_Placenta_PREDO_wa_finalnzeroT$variable))
perc_vars_Placenta_PREDO_wa <- 
  ggplot(summary_Placenta_PREDO_wa_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("% occurence in models with nzero coefficients = 8")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("variable")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()

perc_vars_Placenta_PREDO_wa

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Placenta_PREDO_wa_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/varsPercent_Placenta_PREDO.png", width=1100, height=1400, res=300)
perc_vars_Placenta_PREDO_wa
dev.off()
```r
pm2_Placenta_PREDO_wa_coef <-
  dcast(pm2_Placenta_PREDO_wa[,
                                as.list(unlist(
                                  lapply(.SD,
                                         function(x) {
                                           y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                           list(\non_zero\ = 100 * mean(x != 0),
                                                lcl = y[1],
                                                ucl = y[2],
                                                width = diff(y),
                                                median = median(x[x!= 0]))
                                         }))),
                                .SDcols = c(\Child_Sexfemale\, \Birth_Weight\, \Birth_Length\, \Head_Circumference_at_Birth\, \Delivery_Mode_dichotomaided\, \inducedlabourYes\, \Parity_dichotomgiven birth before\, \Maternal_Age_18PopRegandBR\, \Maternal_PrepregnancyBMI18oct28new\, \maternal_hypertension_dichotomhypertension in current pregnancy\,\maternal_diabetes_dichotomdiabetes in current pregnancy\,\Maternal_Mental_Disorders_By_ChildbirthYes\,\smoking_dichotomyes\,\Alcohol_Use_In_Early_Pregnancy_19Octyes\),
                                by = nzero][order(nzero)] %>%
          melt(id.var = \nzero\) %>%
          .[, metric := sub(\^.+\\.(.+)$\, \\\1\, variable)] %>%
          .[, variable := sub(\^(.+)\\..+$\, \\\1\, variable)] %>%
          .[nzero == nzero_final_placenta_predo_wa], nzero+ variable ~ metric, value.var=\value\)

# get desired order of predictors
pm2_Placenta_PREDO_wa_coef <-
  pm2_Placenta_PREDO_wa_coef[match(c(\Child_Sexfemale\, \Birth_Weight\, \Birth_Length\, \Head_Circumference_at_Birth\, \Delivery_Mode_dichotomaided\, \inducedlabourYes\, \Parity_dichotomgiven birth before\, \Maternal_Age_18PopRegandBR\, \Maternal_PrepregnancyBMI18oct28new\, \maternal_hypertension_dichotomhypertension in current pregnancy\,\maternal_diabetes_dichotomdiabetes in current pregnancy\,\Maternal_Mental_Disorders_By_ChildbirthYes\,\smoking_dichotomyes\,\Alcohol_Use_In_Early_Pregnancy_19Octyes\), pm2_Placenta_PREDO_wa_coef$variable),]
pm2_Placenta_PREDO_wa_coef$variable <- factor(pm2_Placenta_PREDO_wa_coef$variabl, levels=unique(pm2_Placenta_PREDO_wa_coef$variable))

## NOTE: median is used here instead of mean
# make frame for only significant variables:
pm2_Placenta_PREDO_wa_datable <- dcast(pm2_Placenta_PREDO_wa[,
                                                                 as.list(unlist(
                                                                   lapply(.SD,
                                                                          function(x) {
                                                                            y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                                                            list(\non_zero\ = 100 * mean(x != 0),
                                                                                 lcl = y[1],
                                                                                 ucl = y[2],
                                                                                 width = diff(y),
                                                                                 median = median(x[x!= 0]))
                                                                          }))),
                                                                 .SDcols = c(\Child_Sexfemale\, \Birth_Weight\, \Birth_Length\, \Head_Circumference_at_Birth\, \Delivery_Mode_dichotomaided\, \inducedlabourYes\, \Parity_dichotomgiven birth before\, \Maternal_Age_18PopRegandBR\, \Maternal_PrepregnancyBMI18oct28new\, \maternal_hypertension_dichotomhypertension in current pregnancy\,\maternal_diabetes_dichotomdiabetes in current pregnancy\,\Maternal_Mental_Disorders_By_ChildbirthYes\,\smoking_dichotomyes\,\Alcohol_Use_In_Early_Pregnancy_19Octyes\),
                                                                 by = nzero][order(nzero)] %>%
                                           melt(id.var = \nzero\) %>%
                                           .[, metric := sub(\^.+\\.(.+)$\, \\\1\, variable)] %>%
                                           .[, variable := sub(\^(.+)\\..+$\, \\\1\, variable)] %>%
                                           # print %>%
                                           .[nzero == nzero_final_placenta_predo_wa& variable %in% sig_var_names_Placenta_PREDO_wa_finalnzero], nzero+ variable ~ metric, value.var=\value\)

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```r
```r
sig_vars_Placenta_PREDO_wa <-
  pm2_Placenta_PREDO_wa_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x=\nzero\)+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf(\%0.2f\, round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = \purple\, low = \black\, midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c(\child sex\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\, \maternal alcohol use (yes)\))+
  ggplot2::labs(y=\predictor\, x = \number of non-zero coefficients = 9\, color=\%\)

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```r
coef_Placenta_PREDO_wa <- 
  ggplot(pm2_Placenta_PREDO_wa_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.5,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Placenta_PREDO_wa
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/coef_Placenta_PREDO.png", width=2800, height=1400, res=400)
coef_Placenta_PREDO_wa
dev.off()
p1 <-
  csummary_Placenta_PREDO_wa %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
coef_Placenta_PREDO_wa <- 
  ggplot(pm2_Placenta_PREDO_wa_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.5,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  ggtitle("nzero = 9")+
  theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_coef_Placenta_PREDO.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()

to the top

Placenta elastic net splitted by sex

model without alcohol variable, but splitted by sex

males

```r
# in case you want to start from here
load(\InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_n.Rdata\)
Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_n$Child_Sex <- NULL

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```r
```r
yrc_mat_PREDO_Placenta_male_n <- matrix(Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_n$EAAR_Lee)
xrc_mat_PREDO_Placenta_male_n <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_n)[, -1]
yrc_mat_PREDO_scaled_Placenta_male_n <- scale(yrc_mat_PREDO_Placenta_male_n)
xrc_mat_PREDO_scaled_Placenta_male_n <- scale(xrc_mat_PREDO_Placenta_male_n)

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<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   bootstraps_Placenta_male_PREDO_n <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_PREDO_scaled_Placenta_male_n), replace = TRUE) -->
<!--     ensr(xrc_mat_PREDO_scaled_Placenta_male_n[rws, ], yrc_mat_PREDO_scaled_Placenta_male_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->
<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Placenta_male_PREDO_n, file="InputData/Data_ElasticNets/bootstraps_Placenta_male_PREDO_n_1000.Rdata") -->
<!-- ``` -->



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```r
```r
load(\InputData/Data_ElasticNets/bootstraps_Placenta_male_PREDO_n_1000.Rdata\)

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```r
summaries_Placenta_male_PREDO_n <-
  bootstraps_Placenta_male_PREDO_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Placenta_male_PREDO_n
summaries_Placenta_male_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/bootstraps_Placenta_PREDO_MALE.png", width=800, height=600)
summaries_Placenta_male_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```r
load(\InputData/Data_ElasticNets/pm2_Placenta_male_PREDO_n.Rdata\)
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap

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```r
csummary_Placenta_male_PREDO_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                                       list(pm2_Placenta_male_PREDO_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"), by = nzero]
                                            ,
                                            pm2_Placenta_male_PREDO_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                            pm2_Placenta_male_PREDO_n[, .(median_cvm = median(cvm)), by = nzero]
                                       ))[order(nzero)]

csummary_Placenta_male_PREDO_n
g1_Placenta_male_PREDO_n <-
  csummary_Placenta_male_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
  

g2_Placenta_male_PREDO_n <-
  csummary_Placenta_male_PREDO_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Placenta_male_PREDO_n, g2_Placenta_male_PREDO_n, ncol = 1)
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/bootstrapModels_Placenta_PREDO_male.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Placenta_male_PREDO_n, g2_Placenta_male_PREDO_n, ncol = 1)
dev.off()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/Model_Placenta_PREDO_male.png", width=2800, height=1400, res=400)
g1_Placenta_male_PREDO_n
dev.off()
elbow_finder(csummary_Placenta_male_PREDO_n$nzero, csummary_Placenta_male_PREDO_n$median_cvm)

nzero_indices_Cord <- data.frame(t(elbow_finder(csummary_Placenta_male_PREDO_n$nzero, csummary_Placenta_male_PREDO_n$median_cvm)))
colnames(nzero_indices_Cord) <- c("x", "y")
rownames(nzero_indices_Cord) <- NULL
```r
nzero_final_placenta_male <- 5

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```r
csummary_Placenta_male_PREDO_n[nzero %in% nzero_final_placenta_male]
nonzero_choose_Placenta_male <- ggplot2::ggplot(csummary_Placenta_male_PREDO_n) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_Cord, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median of minimum cross-validation errors over bootstraps")+
  ggplot2::xlab("number of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[13], yend = median_cvm[13], colour = "segment"), data = csummary_Placenta_male_PREDO_n, show.legend = F)

nonzero_choose_Placenta_male
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/nzero_choose_Placenta_PREDO_male.png", width=1600, height=1400, res=300)
nonzero_choose_Placenta_male
dev.off()
```r
summary_Placenta_male_PREDO_n_finalnzero <- csummary_Placenta_male_PREDO_n[nzero %in% nzero_final_placenta_male]
sig_var_names_Placenta_male_PREDO_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_Placenta_male_PREDO_n_finalnzero[,!c(\nzero\, \mean_cvm\, \median_cvm\)]) %>% colnames()
colnames(summary_Placenta_male_PREDO_n_finalnzero) <- c(\non-zero\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\, \mean cvm\, \median cvm\)
summary_Placenta_male_PREDO_n_finalnzeroT <- as.data.frame(t(summary_Placenta_male_PREDO_n_finalnzero[,-c(\non-zero\, \median cvm\, \mean cvm\)]))
summary_Placenta_male_PREDO_n_finalnzeroT$variable <- rownames(summary_Placenta_male_PREDO_n_finalnzeroT)
rownames(summary_Placenta_male_PREDO_n_finalnzeroT) <- NULL
names(summary_Placenta_male_PREDO_n_finalnzeroT)[names(summary_Placenta_male_PREDO_n_finalnzeroT) == 'V1'] <- 'percent'
summary_Placenta_male_PREDO_n_finalnzeroT <- summary_Placenta_male_PREDO_n_finalnzeroT[order(summary_Placenta_male_PREDO_n_finalnzeroT$percent),]

summary_Placenta_male_PREDO_n_finalnzeroT$number <- seq(1, length(summary_Placenta_male_PREDO_n_finalnzeroT$variable))

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```r
perc_vars_Placenta_male_PREDO_n <- 
  ggplot(summary_Placenta_male_PREDO_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("% occurence in models with nzero coefficients = 5")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("variable")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()

perc_vars_Placenta_male_PREDO_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Placenta_male_PREDO_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/varsPercent_Placenta_male.png", width=1100, height=1400, res=300)
perc_vars_Placenta_male_PREDO_n
dev.off()
pm2_Placenta_male_PREDO_n_coef <-
  dcast(pm2_Placenta_male_PREDO_n[,
                                as.list(unlist(
                                  lapply(.SD,
                                         function(x) {
                                           y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                           list("non_zero" = 100 * mean(x != 0),
                                                lcl = y[1],
                                                ucl = y[2],
                                                width = diff(y),
                                                median = median(x[x!= 0]))
                                         }))),
                                .SDcols = c("Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"),
                                by = nzero][order(nzero)] %>%
          melt(id.var = "nzero") %>%
          .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
          .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
          .[nzero ==nzero_final_placenta_male], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_Placenta_male_PREDO_n_coef <-
  pm2_Placenta_male_PREDO_n_coef[match(c("Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"), pm2_Placenta_male_PREDO_n_coef$variable),]
pm2_Placenta_male_PREDO_n_coef$variable <- factor(pm2_Placenta_male_PREDO_n_coef$variabl, levels=unique(pm2_Placenta_male_PREDO_n_coef$variable))

## NOTE: median is used here instead of mean
# make frame for only significant variables:
pm2_Placenta_male_PREDO_n_datable <- dcast(pm2_Placenta_male_PREDO_n[,
                                                                 as.list(unlist(
                                                                   lapply(.SD,
                                                                          function(x) {
                                                                            y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                                                            list("non_zero" = 100 * mean(x != 0),
                                                                                 lcl = y[1],
                                                                                 ucl = y[2],
                                                                                 width = diff(y),
                                                                                 median = median(x[x!= 0]))
                                                                          }))),
                                                                 .SDcols = c("Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"),
                                                                 by = nzero][order(nzero)] %>%
                                           melt(id.var = "nzero") %>%
                                           .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
                                           .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
                                           # print %>%
                                           .[nzero == nzero_final_placenta_male & variable %in% sig_var_names_Placenta_male_PREDO_n_finalnzero], nzero+ variable ~ metric, value.var="value")

pm2_Placenta_male_PREDO_n_datable
```r
sig_vars_Placenta_male_PREDO_n <-
  pm2_Placenta_male_PREDO_n_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x=\nzero\)+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf(\%0.2f\, round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = \purple\, low = \black\, midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c(\birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\))+
  ggplot2::labs(y=\predictor\, x = \number of non-zero coefficients = 5\, color=\%\)

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```r
coef_Placenta_male_PREDO_n <- 
  ggplot(pm2_Placenta_male_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Placenta_male_PREDO_n
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/coef_Placenta_PREDO_male.png", width=2800, height=1400, res=400)
coef_Placenta_male_PREDO_n
dev.off()
p1 <-
  csummary_Placenta_male_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
coef_Placenta_male_PREDO_n <- 
  ggplot(pm2_Placenta_male_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  ggtitle("nzero = 5")+
  theme(text = element_text(size = 17), axis.title.x= element_text(size=13), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/Model_coef_Placenta_PREDO_male.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()

to the top

females

```r
# in case you want to start from here
load(\InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_n.Rdata\)
Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_n$Child_Sex <- NULL

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```r
```r
yrc_mat_PREDO_Placenta_female_n <- matrix(Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_n$EAAR_Lee)
xrc_mat_PREDO_Placenta_female_n <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_n)[, -1]
yrc_mat_PREDO_scaled_Placenta_female_n <- scale(yrc_mat_PREDO_Placenta_female_n)
xrc_mat_PREDO_scaled_Placenta_female_n <- scale(xrc_mat_PREDO_Placenta_female_n)

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<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   bootstraps_Placenta_female_PREDO_n <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_PREDO_scaled_Placenta_female_n), replace = TRUE) -->
<!--     ensr(xrc_mat_PREDO_scaled_Placenta_female_n[rws, ], yrc_mat_PREDO_scaled_Placenta_female_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->
<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Placenta_female_PREDO_n, file="InputData/Data_ElasticNets/bootstraps_Placenta_female_PREDO_n_1000.Rdata") -->
<!-- ``` -->



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```r
```r
load(\InputData/Data_ElasticNets/bootstraps_Placenta_female_PREDO_n_1000.Rdata\)

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```r
summaries_Placenta_female_PREDO_n <-
  bootstraps_Placenta_female_PREDO_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Placenta_female_PREDO_n
summaries_Placenta_female_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/bootstraps_Placenta_PREDO_female.png", width=800, height=600)
summaries_Placenta_female_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```r
load(\InputData/Data_ElasticNets/pm2_Placenta_female_PREDO_n.Rdata\)
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap

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```r
csummary_Placenta_female_PREDO_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                                         list(pm2_Placenta_female_PREDO_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"), by = nzero]
                                              ,
                                              pm2_Placenta_female_PREDO_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                              pm2_Placenta_female_PREDO_n[, .(median_cvm = median(cvm)), by = nzero]
                                         ))[order(nzero)]

csummary_Placenta_female_PREDO_n
g1_Placenta_female_PREDO_n <-
  csummary_Placenta_female_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

g2_Placenta_female_PREDO_n <-
  csummary_Placenta_female_PREDO_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Placenta_female_PREDO_n, g2_Placenta_female_PREDO_n, ncol = 1)
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/bootstrapModels_Placenta_PREDO_female.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Placenta_female_PREDO_n, g2_Placenta_female_PREDO_n, ncol = 1)
dev.off()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/Model_Placenta_PREDO_female.png", width=2800, height=1400, res=400)
g1_Placenta_female_PREDO_n
dev.off()
elbow_finder(csummary_Placenta_female_PREDO_n$nzero, csummary_Placenta_female_PREDO_n$median_cvm)

nzero_indices_Cord <- data.frame(t(elbow_finder(csummary_Placenta_female_PREDO_n$nzero, csummary_Placenta_female_PREDO_n$median_cvm)))
colnames(nzero_indices_Cord) <- c("x", "y")
rownames(nzero_indices_Cord) <- NULL
```r
nzero_final_placenta_female <- 6

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```r
csummary_Placenta_female_PREDO_n[nzero %in% nzero_final_placenta_female]
nonzero_choose_Placenta_female <- ggplot2::ggplot(csummary_Placenta_female_PREDO_n) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_Cord, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median of minimum cross-validation errors over bootstraps")+
  ggplot2::xlab("number of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[13], yend = median_cvm[13], colour = "segment"), data = csummary_Placenta_female_PREDO_n, show.legend = F)

nonzero_choose_Placenta_female
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/nzero_choose_Placenta_PREDO_female.png", width=1600, height=1400, res=300)
nonzero_choose_Placenta_female
dev.off()
```r
summary_Placenta_female_PREDO_n_finalnzero <- csummary_Placenta_female_PREDO_n[nzero %in% nzero_final_placenta_female]
sig_var_names_Placenta_female_PREDO_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_Placenta_female_PREDO_n_finalnzero[,!c(\nzero\, \mean_cvm\, \median_cvm\)]) %>% colnames()
colnames(summary_Placenta_female_PREDO_n_finalnzero) <- c(\non-zero\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\, \mean cvm\, \median cvm\)
summary_Placenta_female_PREDO_n_finalnzeroT <- as.data.frame(t(summary_Placenta_female_PREDO_n_finalnzero[,-c(\non-zero\, \median cvm\, \mean cvm\)]))
summary_Placenta_female_PREDO_n_finalnzeroT$variable <- rownames(summary_Placenta_female_PREDO_n_finalnzeroT)
rownames(summary_Placenta_female_PREDO_n_finalnzeroT) <- NULL
names(summary_Placenta_female_PREDO_n_finalnzeroT)[names(summary_Placenta_female_PREDO_n_finalnzeroT) == 'V1'] <- 'percent'
summary_Placenta_female_PREDO_n_finalnzeroT <- summary_Placenta_female_PREDO_n_finalnzeroT[order(summary_Placenta_female_PREDO_n_finalnzeroT$percent),]

summary_Placenta_female_PREDO_n_finalnzeroT$number <- seq(1, length(summary_Placenta_female_PREDO_n_finalnzeroT$variable))

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```r
perc_vars_Placenta_female_PREDO_n <- 
  ggplot(summary_Placenta_female_PREDO_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("% occurence in models with nzero coefficients = 4")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("variable")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()

perc_vars_Placenta_female_PREDO_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Placenta_female_PREDO_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/varsPercent_Placenta_female.png", width=1100, height=1400, res=300)
perc_vars_Placenta_female_PREDO_n
dev.off()
pm2_Placenta_female_PREDO_n_coef <-
  dcast(pm2_Placenta_female_PREDO_n[,
                                  as.list(unlist(
                                    lapply(.SD,
                                           function(x) {
                                             y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                             list("non_zero" = 100 * mean(x != 0),
                                                  lcl = y[1],
                                                  ucl = y[2],
                                                  width = diff(y),
                                                  median = median(x[x!= 0]))
                                           }))),
                                  .SDcols = c("Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"),
                                  by = nzero][order(nzero)] %>%
          melt(id.var = "nzero") %>%
          .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
          .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
          .[nzero ==nzero_final_placenta_female], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_Placenta_female_PREDO_n_coef <-
  pm2_Placenta_female_PREDO_n_coef[match(c("Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"), pm2_Placenta_female_PREDO_n_coef$variable),]
pm2_Placenta_female_PREDO_n_coef$variable <- factor(pm2_Placenta_female_PREDO_n_coef$variabl, levels=unique(pm2_Placenta_female_PREDO_n_coef$variable))

## NOTE: median is used here instead of mean
# make frame for only significant variables:
pm2_Placenta_female_PREDO_n_datable <- dcast(pm2_Placenta_female_PREDO_n[,
                                                                     as.list(unlist(
                                                                       lapply(.SD,
                                                                              function(x) {
                                                                                y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                                                                list("non_zero" = 100 * mean(x != 0),
                                                                                     lcl = y[1],
                                                                                     ucl = y[2],
                                                                                     width = diff(y),
                                                                                     median = median(x[x!= 0]))
                                                                              }))),
                                                                     .SDcols = c("Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"),
                                                                     by = nzero][order(nzero)] %>%
                                             melt(id.var = "nzero") %>%
                                             .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
                                             .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
                                             # print %>%
                                             .[nzero == nzero_final_placenta_female & variable %in% sig_var_names_Placenta_female_PREDO_n_finalnzero], nzero+ variable ~ metric, value.var="value")

pm2_Placenta_female_PREDO_n_datable
```r
sig_vars_Placenta_female_PREDO_n <-
  pm2_Placenta_female_PREDO_n_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x=\nzero\)+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf(\%0.2f\, round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = \purple\, low = \black\, midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c(\birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\))+
  ggplot2::labs(y=\predictor\, x = \number of non-zero coefficients = 6\, color=\%\)

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```r
coef_Placenta_female_PREDO_n <- 
  ggplot(pm2_Placenta_female_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.5,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Placenta_female_PREDO_n
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/coef_Placenta_PREDO_female.png", width=2800, height=1400, res=400)
coef_Placenta_female_PREDO_n
dev.off()
p1 <-
  csummary_Placenta_female_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
coef_Placenta_male_PREDO_n <- 
  ggplot(pm2_Placenta_female_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.5,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  ggtitle("nzero = 6")+
  theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/Model_coef_Placenta_PREDO_female.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()

to the top

Prediction in PREDO cord blood

ifelse(!dir.exists(file.path(getwd(), "Results/Figures/predPREDO")), dir.create(file.path(getwd(), "Results/Figures/predPREDO")), FALSE)

load PREDO data EPIC

```r
load(\InputData/ClockCalculationsInput/Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n.Rdata\)

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load PREDO data 450K

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```r
```r
load(\InputData/ClockCalculationsInput/Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n.Rdata\)

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load beta values from ITU

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```r
load("InputData/Data_ElasticNets/Beta_Cord_ITU_n.Rdata")
Beta_Cord_ITU_n

prepare PREDO data EPIC

```r
y_mat_PREDO_Cord_pred <- matrix(Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n$EAAR_Bohlin)

Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_vars <- Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n[ ,c(\Child_Sex\, \Birth_Length\, \Delivery_Mode_dichotom\, \Maternal_Mental_Disorders_By_Childbirth\, \smoking_dichotom\)]

x_mat_PREDO_Cord_pred <- model.matrix(~ ., data= Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_vars)[, -1]

y_mat_PREDO_scaled_Cord_pred <- scale(y_mat_PREDO_Cord_pred)

x_mat_PREDO_scaled_Cord_pred <- scale(x_mat_PREDO_Cord_pred)
x_mat_PREDO_scaled_Cord_pred <- cbind(1, x_mat_PREDO_scaled_Cord_pred)
colnames(x_mat_PREDO_scaled_Cord_pred) <- c(\Intercept\, \child sex\, \birth length\, \delivery mode\, \maternal mental disorders\, \maternal smoking\)

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prepare PREDO data 450K

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```r
```r
y_mat_PREDO_Cord_predK <- matrix(Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n$EAAR_Bohlin)

Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_varsK <- Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n[ ,c(\Child_Sex\, \Birth_Length\, \Delivery_Mode_dichotom\,  \Maternal_Mental_Disorders_By_Childbirth\, \smoking_dichotom\)]

x_mat_PREDO_Cord_predK <- model.matrix(~ ., data= Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_varsK)[, -1]

y_mat_PREDO_scaled_Cord_predK <- scale(y_mat_PREDO_Cord_predK)

x_mat_PREDO_scaled_Cord_predK <- scale(x_mat_PREDO_Cord_predK)
x_mat_PREDO_scaled_Cord_predK <- cbind(1, x_mat_PREDO_scaled_Cord_predK)
colnames(x_mat_PREDO_scaled_Cord_predK) <- c(\Intercept\, \child sex\, \birth length\, \delivery mode\,\maternal mental disorders\, \maternal smoking\)

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matrix multiplication EPIC

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```r
```r
#Y=X*B
y_pred_PREDO_cord <- x_mat_PREDO_scaled_Cord_pred %*% Beta_Cord_ITU_n

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matrix multiplication 450K

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```r
```r
#Y=X*B
y_pred_PREDO_cordK <- x_mat_PREDO_scaled_Cord_predK %*% Beta_Cord_ITU_n

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data EPIC

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```r
```r
PREDO_cord_pred_exp_real <- data.frame(cbind(y_pred_PREDO_cord, y_mat_PREDO_scaled_Cord_pred))
names(PREDO_cord_pred_exp_real) <- c(\predicted_EAAR\, \real_EAAR\)

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data 450K

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```r
```r
PREDO_cord_pred_exp_realK <- data.frame(cbind(y_pred_PREDO_cordK, y_mat_PREDO_scaled_Cord_predK))
names(PREDO_cord_pred_exp_realK) <- c(\predicted_EAAR\, \real_EAAR\)

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cor EPIC

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```r
cor.test(PREDO_cord_pred_exp_real$predicted_EAAR,PREDO_cord_pred_exp_real$real_EAAR, alternative="greater")
# n = 144

plot_pred_real_epic <- ggscatter(PREDO_cord_pred_exp_real, x = "predicted_EAAR", y = "real_EAAR", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "predicted EAAR", ylab = "true EAAR", subtitle="PREDO EPIC (n=144)")+
   stat_cor(label.x = -0.4, label.y=3,p.accuracy = 0.001, r.accuracy = 0.01, alternative="greater")+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_text(size=12),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(-3,3), breaks = seq(-3,3, by=1))+
  scale_x_continuous(limits = c(-0.4,0.6), breaks = seq(-0.4,0.6, by=0.2))

r(142) = .24, p=0.002 n=144

cor 450K

cor.test(PREDO_cord_pred_exp_realK$predicted_EAAR,PREDO_cord_pred_exp_realK$real_EAAR, alternative="greater")

plot_pred_real_450k <- ggscatter(PREDO_cord_pred_exp_realK, x = "predicted_EAAR", y = "real_EAAR", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "predicted EAAR", ylab = "true EAAR", subtitle="PREDO 450K (n=766)")+
   stat_cor(label.x = -0.4, label.y=4, p.accuracy = 0.001, r.accuracy = 0.01, alternative="greater")+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_text(size=12),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(-4.5,4), breaks = seq(-4,4, by=1))+
  scale_x_continuous(limits = c(-0.5,0.8), breaks = seq(-0.4,0.8, by=0.2))
# n = 796

r(764) = .11, p=0.002 n=766

ggarrange(plot_pred_real_epic, plot_pred_real_450k, nrow=1)
png(file="Results/Figures/predPREDO/predictionEAARcord.png", width= 3600, height=2100, res=480)
ggarrange(plot_pred_real_epic, plot_pred_real_450k, nrow=1)
dev.off()

pdf(file="Results/Figures/predPREDO/predictionEAARcord.pdf", width= 10, height=5)
ggarrange(plot_pred_real_epic, plot_pred_real_450k, nrow=1)
dev.off()

elastic net PREDO EPIC Cord blood

main model, without alcohol

```r
load(\InputData/ClockCalculationsInput/Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n.Rdata\)

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```r
```r
yrc_mat_PREDO_Cord_n <- matrix(Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n$EAAR_Bohlin)
xrc_mat_PREDO_Cord_n <- model.matrix( ~ . - EAAR_Bohlin, data = Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n)[, -1]
yrc_mat_PREDO_scaled_Cord_n <- scale(yrc_mat_PREDO_Cord_n)
xrc_mat_PREDO_scaled_Cord_n <- scale(xrc_mat_PREDO_Cord_n)

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<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Cord_PREDO_n <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_PREDO_scaled_Cord_n), replace = TRUE) -->
<!--     ensr(xrc_mat_PREDO_scaled_Cord_n[rws, ], yrc_mat_PREDO_scaled_Cord_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!--   end_time <- Sys.time() -->
<!--   end_time - start_time -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Cord_PREDO_n, file="InputData/Data_ElasticNets/bootstraps_Cord_PREDO_n_1000.Rdata") -->
<!-- ``` -->



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```r
```r
load(\InputData/Data_ElasticNets/bootstraps_Cord_PREDO_n_1000.Rdata\)

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first get a summary of all ensr objects

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```r
summaries_Cord_PREDO_n <-
  bootstraps_Cord_PREDO_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Cord_PREDO_n
summaries_Cord_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()+
  ggplot2::labs(x="\nnzero", y="cvm\n")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))+
  ggplot2::theme_bw()
  
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstraps_Cord_PREDO.png", width=2200, height=1400, res=300)
summaries_Cord_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()+
  ggplot2::labs(x="\nnzero", y="cvm\n")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))+
  ggplot2::theme_bw()
dev.off()
```r
load(\InputData/Data_ElasticNets/pm2_Cord_PREDO_n.Rdata\)
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap

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look how often a particular variable is associated with a non-zero coefficient in a model with a given number of non-zero coefficients (over all bootstraps)


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```r
csummary_Cord_PREDO_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                               list(pm2_Cord_PREDO_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy", "maternal_diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_Disorders_By_ChildbirthYes", "smoking_dichotomyes"), by = nzero]
                                    ,
                                    pm2_Cord_PREDO_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                    pm2_Cord_PREDO_n[, .(median_cvm = median(cvm)), by = nzero]
                               ))[order(nzero)]

csummary_Cord_PREDO_n
g1_Cord_PREDO_n <-
  csummary_Cord_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex", "birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor\n", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
  

g2_Cord_PREDO_n <-
  csummary_Cord_PREDO_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "nzero")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Cord_PREDO_n, g2_Cord_PREDO_n, ncol = 1)
g1_Cord_PREDO_n
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_Cord_PREDO.png", width=2800, height=1400, res=400)
g1_Cord_PREDO_n
dev.off()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstrapModels_Cord_PREDO.png", width=2800, height=1400, res=300)
gridExtra::grid.arrange(g1_Cord_PREDO_n, g2_Cord_PREDO_n, ncol = 1)
dev.off()
elbow_finder(csummary_Cord_PREDO_n$nzero, csummary_Cord_PREDO_n$median_cvm)

nzero_indices_Cord <- data.frame(t(elbow_finder(csummary_Cord_PREDO_n$nzero, csummary_Cord_PREDO_n$median_cvm)))
colnames(nzero_indices_Cord) <- c("x", "y")
rownames(nzero_indices_Cord) <- NULL
```r
nzero_final_cord_predo <- 7

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```r
csummary_Cord_PREDO_n[nzero %in% nzero_final_cord_predo]
```r
summary_Cord_PREDO_n_finalnzero <- csummary_Cord_PREDO_n[nzero %in% nzero_final_cord_predo]
sig_var_names_Cord_PREDO_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_Cord_PREDO_n_finalnzero[,!c(\nzero\, \mean_cvm\, \median_cvm\)]) %>% colnames()
colnames(summary_Cord_PREDO_n_finalnzero) <- c(\non-zero\, \child sex (female)\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\, \mean cvm\, \median cvm\)
summary_Cord_PREDO_n_finalnzeroT <- as.data.frame(t(summary_Cord_PREDO_n_finalnzero[,-c(\non-zero\, \median cvm\, \mean cvm\)]))
summary_Cord_PREDO_n_finalnzeroT$variable <- rownames(summary_Cord_PREDO_n_finalnzeroT)
rownames(summary_Cord_PREDO_n_finalnzeroT) <- NULL
names(summary_Cord_PREDO_n_finalnzeroT)[names(summary_Cord_PREDO_n_finalnzeroT) == 'V1'] <- 'percent'
summary_Cord_PREDO_n_finalnzeroT <- summary_Cord_PREDO_n_finalnzeroT[order(summary_Cord_PREDO_n_finalnzeroT$percent),]

summary_Cord_PREDO_n_finalnzeroT$number <- seq(1, length(summary_Cord_PREDO_n_finalnzeroT$variable))

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```r
perc_vars_Cord_PREDO_n <- 
  ggplot(summary_Cord_PREDO_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("\n% occurence in models with nzero coefficients = 9    ")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("predictor\n")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

perc_vars_Cord_PREDO_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Cord_PREDO_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])
```r
pm2_Cord_PREDO_n_coef <-
  dcast(pm2_Cord_PREDO_n[,
                                as.list(unlist(
                                  lapply(.SD,
                                         function(x) {
                                           y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                           list(\non_zero\ = 100 * mean(x != 0),
                                                lcl = y[1],
                                                ucl = y[2],
                                                width = diff(y),
                                                median = median(x[x!= 0]))
                                         }))),
                                .SDcols = c(\Child_Sexfemale\, \Birth_Weight\, \Birth_Length\, \Head_Circumference_at_Birth\, \Delivery_Mode_dichotomaided\, \inducedlabourYes\, \Parity_dichotomgiven birth before\, \Maternal_Age_18PopRegandBR\, \Maternal_PrepregnancyBMI18oct28new\, \maternal_hypertension_dichotomhypertension in current pregnancy\, \maternal_diabetes_dichotomdiabetes in current pregnancy\, \Maternal_Mental_Disorders_By_ChildbirthYes\, \smoking_dichotomyes\),
                                by = nzero][order(nzero)] %>%
          melt(id.var = \nzero\) %>%
          .[, metric := sub(\^.+\\.(.+)$\, \\\1\, variable)] %>%
          .[, variable := sub(\^(.+)\\..+$\, \\\1\, variable)] %>%
          .[nzero ==nzero_final_cord_predo], nzero+ variable ~ metric, value.var=\value\)

# get desired order of predictors
pm2_Cord_PREDO_n_coef <-
  pm2_Cord_PREDO_n_coef[match(c(\Child_Sexfemale\, \Birth_Weight\, \Birth_Length\, \Head_Circumference_at_Birth\, \Delivery_Mode_dichotomaided\, \inducedlabourYes\, \Parity_dichotomgiven birth before\, \Maternal_Age_18PopRegandBR\, \Maternal_PrepregnancyBMI18oct28new\, \maternal_hypertension_dichotomhypertension in current pregnancy\, \maternal_diabetes_dichotomdiabetes in current pregnancy\, \Maternal_Mental_Disorders_By_ChildbirthYes\, \smoking_dichotomyes\), pm2_Cord_PREDO_n_coef$variable),]
pm2_Cord_PREDO_n_coef$variable <- factor(pm2_Cord_PREDO_n_coef$variabl, levels=unique(pm2_Cord_PREDO_n_coef$variable))

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```r
```r
write_xlsx(pm2_Cord_PREDO_n_coef,\Results/Tables/Coefficients_Cord_PREDO.xlsx\)

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```r
coef_Cord_PREDO_n <- 
  ggplot(pm2_Cord_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Cord_PREDO_n
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/coef_Cord_PREDO.png",  width=2800, height=1400, res=400)
coef_Cord_PREDO_n
dev.off()
p1 <-
  csummary_Cord_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor\n", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
  ggplot(pm2_Cord_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
   ggtitle("nzero = 7")+
  theme_bw()+
 theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_coef_Cord_PREDO.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()

to the top

elastic net PREDO 450K Cord blood

main model, without alcohol

```r
load(\InputData/ClockCalculationsInput/Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n.Rdata\)

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```r
```r
yrc_mat_PREDO_Cord450_n <- matrix(Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n$EAAR_Bohlin)
xrc_mat_PREDO_Cord450_n <- model.matrix( ~ . - EAAR_Bohlin, data = Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n)[, -1]
yrc_mat_PREDO_scaled_Cord450_n <- scale(yrc_mat_PREDO_Cord450_n)
xrc_mat_PREDO_scaled_Cord450_n <- scale(xrc_mat_PREDO_Cord450_n)

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<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Cord450_PREDO_n <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_PREDO_scaled_Cord450_n), replace = TRUE) -->
<!--     ensr(xrc_mat_PREDO_scaled_Cord450_n[rws, ], yrc_mat_PREDO_scaled_Cord450_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!--   end_time <- Sys.time() -->
<!--   end_time - start_time -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Cord450_PREDO_n, file="InputData/Data_ElasticNets/bootstraps_Cord450_PREDO_n_1000.Rdata") -->
<!-- ``` -->



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```r
```r
load(\InputData/Data_ElasticNets/bootstraps_Cord450_PREDO_n_1000.Rdata\)

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```r
summaries_Cord450_PREDO_n <-
  bootstraps_Cord450_PREDO_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Cord450_PREDO_n
summaries_Cord450_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()+
  ggplot2::labs(x="\nnzero", y="cvm\n")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))+
  ggplot2::theme_bw()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstraps_Cord450.png", width=2200, height=1400, res=300)
summaries_Cord450_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()+
  ggplot2::labs(x="\nnzero", y="cvm\n")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))+
  ggplot2::theme_bw()
dev.off()
```r
load(\InputData/Data_ElasticNets/pm2_Cord450_PREDO_n.Rdata\)
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap

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```r
csummary_Cord450_PREDO_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                               list(pm2_Cord450_PREDO_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy", "maternal_diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_Disorders_By_ChildbirthYes", "smoking_dichotomyes"), by = nzero]
                                    ,
                                    pm2_Cord450_PREDO_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                    pm2_Cord450_PREDO_n[, .(median_cvm = median(cvm)), by = nzero]
                               ))[order(nzero)]

csummary_Cord450_PREDO_n
g1_Cord450_PREDO_n <-
  csummary_Cord450_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex", "birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor\n", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
  

g2_Cord450_PREDO_n <-
  csummary_Cord450_PREDO_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "nzero")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Cord450_PREDO_n, g2_Cord450_PREDO_n, ncol = 1)
g1_Cord450_PREDO_n
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_Cord450_PREDO.png", width=2800, height=1400, res=400)
g1_Cord450_PREDO_n
dev.off()
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstrapModels_Cord450_PREDO.png", width=2800, height=1400, res=300)
gridExtra::grid.arrange(g1_Cord450_PREDO_n, g2_Cord450_PREDO_n, ncol = 1)
dev.off()
elbow_finder(csummary_Cord450_PREDO_n$nzero, csummary_Cord450_PREDO_n$median_cvm)

nzero_indices_Cord450 <- data.frame(t(elbow_finder(csummary_Cord450_PREDO_n$nzero, csummary_Cord450_PREDO_n$median_cvm)))
colnames(nzero_indices_Cord450) <- c("x", "y")
rownames(nzero_indices_Cord450) <- NULL
```r
nzero_final_Cord450_predo <- 6

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```r
csummary_Cord450_PREDO_n[nzero %in% nzero_final_Cord450_predo]
```r
summary_Cord450_PREDO_n_finalnzero <- csummary_Cord450_PREDO_n[nzero %in% nzero_final_Cord450_predo]
sig_var_names_Cord450_PREDO_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_Cord450_PREDO_n_finalnzero[,!c(\nzero\, \mean_cvm\, \median_cvm\)]) %>% colnames()
colnames(summary_Cord450_PREDO_n_finalnzero) <- c(\non-zero\, \child sex (female)\, \birth weight\, \birth length\, \head circumference\, \delivery mode (aided)\, \induced labor (yes)\, \parity (birth before)\, \maternal age\, \maternal BMI\, \maternal hypertension (yes)\, \maternal diabetes (yes)\, \maternal mental disorders (yes)\, \maternal smoking (yes)\, \mean cvm\, \median cvm\)
summary_Cord450_PREDO_n_finalnzeroT <- as.data.frame(t(summary_Cord450_PREDO_n_finalnzero[,-c(\non-zero\, \median cvm\, \mean cvm\)]))
summary_Cord450_PREDO_n_finalnzeroT$variable <- rownames(summary_Cord450_PREDO_n_finalnzeroT)
rownames(summary_Cord450_PREDO_n_finalnzeroT) <- NULL
names(summary_Cord450_PREDO_n_finalnzeroT)[names(summary_Cord450_PREDO_n_finalnzeroT) == 'V1'] <- 'percent'
summary_Cord450_PREDO_n_finalnzeroT <- summary_Cord450_PREDO_n_finalnzeroT[order(summary_Cord450_PREDO_n_finalnzeroT$percent),]

summary_Cord450_PREDO_n_finalnzeroT$number <- seq(1, length(summary_Cord450_PREDO_n_finalnzeroT$variable))

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```r
perc_vars_Cord450_PREDO_n <- 
  ggplot(summary_Cord450_PREDO_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("\n% occurence in models with nzero coefficients = 9    ")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("predictor\n")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

perc_vars_Cord450_PREDO_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Cord450_PREDO_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])
```r
pm2_Cord450_PREDO_n_coef <-
  dcast(pm2_Cord450_PREDO_n[,
                                as.list(unlist(
                                  lapply(.SD,
                                         function(x) {
                                           y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                           list(\non_zero\ = 100 * mean(x != 0),
                                                lcl = y[1],
                                                ucl = y[2],
                                                width = diff(y),
                                                median = median(x[x!= 0]))
                                         }))),
                                .SDcols = c(\Child_Sexfemale\, \Birth_Weight\, \Birth_Length\, \Head_Circumference_at_Birth\, \Delivery_Mode_dichotomaided\, \inducedlabourYes\, \Parity_dichotomgiven birth before\, \Maternal_Age_18PopRegandBR\, \Maternal_PrepregnancyBMI18oct28new\, \maternal_hypertension_dichotomhypertension in current pregnancy\, \maternal_diabetes_dichotomdiabetes in current pregnancy\, \Maternal_Mental_Disorders_By_ChildbirthYes\, \smoking_dichotomyes\),
                                by = nzero][order(nzero)] %>%
          melt(id.var = \nzero\) %>%
          .[, metric := sub(\^.+\\.(.+)$\, \\\1\, variable)] %>%
          .[, variable := sub(\^(.+)\\..+$\, \\\1\, variable)] %>%
          .[nzero ==nzero_final_Cord450_predo], nzero+ variable ~ metric, value.var=\value\)

# get desired order of predictors
pm2_Cord450_PREDO_n_coef <-
  pm2_Cord450_PREDO_n_coef[match(c(\Child_Sexfemale\, \Birth_Weight\, \Birth_Length\, \Head_Circumference_at_Birth\, \Delivery_Mode_dichotomaided\, \inducedlabourYes\, \Parity_dichotomgiven birth before\, \Maternal_Age_18PopRegandBR\, \Maternal_PrepregnancyBMI18oct28new\, \maternal_hypertension_dichotomhypertension in current pregnancy\, \maternal_diabetes_dichotomdiabetes in current pregnancy\, \Maternal_Mental_Disorders_By_ChildbirthYes\, \smoking_dichotomyes\), pm2_Cord450_PREDO_n_coef$variable),]
pm2_Cord450_PREDO_n_coef$variable <- factor(pm2_Cord450_PREDO_n_coef$variabl, levels=unique(pm2_Cord450_PREDO_n_coef$variable))

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```r
```r
write_xlsx(pm2_Cord450_PREDO_n_coef,\Results/Tables/Coefficients_Cord450_PREDO.xlsx\)

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```r
coef_Cord450_PREDO_n <- 
  ggplot(pm2_Cord450_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Cord450_PREDO_n
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/coef_Cord450_PREDO.png",  width=2800, height=1400, res=400)
coef_Cord450_PREDO_n
dev.off()
p1 <-
  csummary_Cord450_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor\n", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
  ggplot(pm2_Cord450_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
   ggtitle("nzero = 6")+
  theme_bw()+
 theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_coef_Cord450_PREDO.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()
```r
rm(list = setdiff(ls(), lsf.str()))

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[to the top](#top) 

# Cross-Tissues

## Correlations DNAmGA {#corTissuesDNAmGA}  

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```r
```r
load(file= \InputData/ClockCalculationsInput/Data_CVS_ITU.Rdata\)
load(file= \InputData/ClockCalculationsInput/Data_Cord_ITU.Rdata\)
load(file= \InputData/ClockCalculationsInput/Data_Placenta_ITU.Rdata\)
load(file=\InputData/ClockCalculationsInput/Data_Full_ITU.Rdata\) # data persons with all measurement points available
load(file=\InputData/ClockCalculationsInput/Data_Cord_Placenta_ITU.Rdata\)
load(file=\InputData/ClockCalculationsInput/Data_CVS_Placenta_ITU.Rdata\)
load(file=\InputData/ClockCalculationsInput/Data_CVS_Cord_ITU.Rdata\)
load(file=\InputData/ClockCalculationsInput/Data_ITU_all.Rdata\) # all persons together in one data frame

load(file= \InputData/ClockCalculationsInput/Data_Placenta_male_ITU.Rdata\)
load(file= \InputData/ClockCalculationsInput/Data_Placenta_female_ITU.Rdata\)

load(file=\InputData/ClockCalculationsInput/Data_PREDO_450Kcord.Rdata\)
load(file=\InputData/ClockCalculationsInput/Data_PREDO_EPICcord.Rdata\)
load(file=\InputData/ClockCalculationsInput/Data_PREDO_EPICplacenta.Rdata\)
load(file=\InputData/ClockCalculationsInput/Data_PREDO_EPIC_Cord_Placenta.Rdata\)
load(file=\InputData/ClockCalculationsInput/Data_PREDO_EPIC_all.Rdata\) # all persons with EPIC data together in one data frame

load(file=\InputData/ClockCalculationsInput/Data_PREDO_Placenta_male.Rdata\)
load(file=\InputData/ClockCalculationsInput/Data_PREDO_Placenta_female.Rdata\)

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*Cord blood & Placenta (in ITU)*

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```r
```r
DNAmGAs_birth <- Data_Cord_Placenta_ITU[ ,c(\DNAmGA_Bohlin\,\DNAmGA_Lee\, \Gestational_Age_Weeks\)]
colnames(DNAmGAs_birth) <- c(\Cordblood\, \Placenta\, \GA_birth\)

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```r
BirthcorrDNAmGAs <- rcorr(as.matrix(DNAmGAs_birth))
BirthcorrDNAmGAs

adjusting for GA at birth

# partial correlation
pcor.test(x=DNAmGAs_birth$Cordblood, y=DNAmGAs_birth$Placenta, z=DNAmGAs_birth$GA_birth)
```r
cor_cord_placenta_dnamga <-ggscatter(Data_Cord_Placenta_ITU, x = \DNAmGA_Bohlin\, y = \DNAmGA_Lee\, 
          add = \reg.line\, conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = \pearson\,
          xlab = \DNAm GA cord blood (weeks)\, ylab = \DNAmGA Placenta (weeks)\, subtitle=\ ITU (n = 390)\)+
   stat_cor(label.x = 32, label.y=44,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_text(size=12),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank())+
  scale_y_continuous(limits = c(30,44), breaks = seq(30,44, by=2))+
 scale_x_continuous(limits = c(32,44), breaks = seq(32,44, by=2))

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```r
png(file="Results/Figures/diffTissues/DNAmGA_Cord_Placenta_ITU.png", width= 2600, height=1600, res=500)
cor_cord_placenta_dnamga
dev.off()

Cord blood and Placenta (in PREDO)

```r
DNAmGAsPREDO <- Data_PREDO_EPIC_Cord_Placenta[ ,c(\DNAmGA_Bohlin\,\DNAmGA_Lee\, \Gestational_Age\)]
colnames(DNAmGAsPREDO) <- c(\Cordblood\, \Placenta\, \GA_birth\)

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```r
allcorrsDNAmGAsPREDO <- rcorr(as.matrix(DNAmGAsPREDO))
allcorrsDNAmGAsPREDO
# partial correlation
pcor.test(x=DNAmGAsPREDO$Cord, y=DNAmGAsPREDO$Placenta, z=DNAmGAsPREDO[,c("GA_birth")])
```r
cor_cord_placenta_dnamga_predo <-ggscatter(Data_PREDO_EPIC_Cord_Placenta, x = \DNAmGA_Bohlin\, y = \DNAmGA_Lee\, 
          add = \reg.line\, conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = \pearson\,
          xlab = \DNAm GA cord blood (weeks)\, ylab = \DNAmGA Placenta (weeks)\, subtitle=\ PREDO (n = 116)\)+
   stat_cor(label.x = 34, label.y=42,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_text(size=12),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(32,42), breaks = seq(32,42, by=2))+
 scale_x_continuous(limits = c(34,42), breaks = seq(34,42, by=2))

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```r
png(file="Results/Figures/diffTissues/DNAmGA_Cord_Placenta_PREDO.png", width= 2600, height=1600, res=500)
cor_cord_placenta_dnamga_predo
dev.off()

CVS and Placenta

```r
DNAmGAs_CP <- Data_CVS_Placenta_ITU[ ,c(\DNAmGA_Lee_CVS\,\DNAmGA_Lee_Placenta\, \gestage_at_CVS_weeks\, \Gestational_Age_Weeks\)]
colnames(DNAmGAs_CP) <- c(\CVS\, \Placenta\, \GA_CVS\, \GA_Birth\)

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```r
CPcorrDNAmGAs <- rcorr(as.matrix(DNAmGAs_CP))
CPcorrDNAmGAs
# partial correlation
pcor.test(x=DNAmGAs_CP$CVS, y=DNAmGAs_CP$Placenta, z=DNAmGAs_CP[,c("GA_CVS","GA_Birth")])
```r
cor_cvs_placenta_dnamga <-ggscatter(Data_CVS_Placenta_ITU, x = \DNAmGA_Lee_CVS\, y = \DNAmGA_Lee_Placenta\, 
          add = \reg.line\, conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = \pearson\,
          xlab = \DNAm GA CVS (weeks)\, ylab = \DNAmGA placenta (weeks)\, subtitle=\ ITU (n = 86)\)+
   stat_cor(label.x = 6, label.y=44, p.accuracy = 0.01, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_text(size=12),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank())+
  scale_y_continuous(limits = c(34,44), breaks = seq(34,44, by=2))+
 scale_x_continuous(limits = c(6,14), breaks = seq(6,14, by=2))

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```r
png(file="Results/Figures/diffTissues/DNAmGA_CVS_Placenta.png", width= 2600, height=1600, res=500)
cor_cvs_placenta_dnamga
dev.off()

CVS and Cord blood

```r
DNAmGAs_CC <- Data_CVS_Cord_ITU[ ,c(\DNAmGA_Lee\,\DNAmGA_Bohlin\, \gestage_at_CVS_weeks\, \Gestational_Age_Weeks\)]
colnames(DNAmGAs_CC) <- c(\CVS\, \Cord blood\, \GA_CVS\, \GA_Birth\)

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```r
CCcorrDNAmGAs <- rcorr(as.matrix(DNAmGAs_CC))
CCcorrDNAmGAs
# partial correlation
pcor.test(x=DNAmGAs_CC$CVS, y=DNAmGAs_CC$Cord, z=DNAmGAs_CC[,c("GA_CVS","GA_Birth")])
```r
cor_cvs_cord_dnamga <- ggscatter(Data_CVS_Cord_ITU, x = \DNAmGA_Lee\, y = \DNAmGA_Bohlin\, 
          add = \reg.line\, conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = \pearson\,
          xlab = \DNAm GA CVS (weeks)\, ylab = \DNAmGA cord blood (weeks)\, subtitle=\ ITU (n = 73)\)+
   stat_cor(label.x = 6, label.y=42,p.accuracy = 0.01, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_text(size=12),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank())+
  scale_y_continuous(limits = c(32,42), breaks = seq(32,42, by=2))+
 scale_x_continuous(limits = c(6,14), breaks = seq(6,14, by=2))

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```r
png(file="Results/Figures/diffTissues/DNAmGA_CVS_Cord.png", width= 2600, height=1600, res=500)
cor_cvs_cord_dnamga
dev.off()

to the top

Correspondence EAAR

Fig. 4 Cord blood & Placenta (in ITU)

```r
DNAmGAResidsCBirth <- Data_Cord_Placenta_ITU[ ,c(\EAAR_Bohlin\,\EAAR_Lee\)]
colnames(DNAmGAResidsCBirth) <- c(\Cordblood\, \Placenta\)

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```r
allcorrsDNAmGAResidCBirth <- rcorr(as.matrix(DNAmGAResidsCBirth))
allcorrsDNAmGAResidCBirth
cor_cord_placenta_resid <- ggscatter(Data_Cord_Placenta_ITU, x = "EAAR_Bohlin", y = "EAAR_Lee", 
          add = "reg.line", conf.int = TRUE, 
          xlab = "EAAR Cord blood", ylab = "EAAR fetal Placenta")+
          stat_cor(method = "pearson", label.x = -2, label.y = 4, r.digits = 1, p.digits = 2)+
          geom_hline(yintercept=0, linetype="dashed")+
          geom_vline(xintercept=0, linetype="dashed")+
  theme(text = element_text(size=13), axis.text.x = element_text(size=13))+
          coord_cartesian(xlim = c(-2, 2), ylim=c(-4,4)) 

cor_cord_placenta_resid

cor_cord_placenta_resid_f <- ggscatter(Data_Cord_Placenta_ITU, x = "EAAR_Bohlin", y = "EAAR_Lee", 
          add = "reg.line", conf.int = TRUE, 
          xlab = "EAAR Cord blood", ylab = "EAAR fetal Placenta")+
          #stat_cor(method = "pearson", label.x = -2.5, label.y = 5, r.digits = 1, p.digits = 3)+
          geom_hline(yintercept=0, linetype="dashed")+
          geom_vline(xintercept=0, linetype="dashed")+
        theme(text = element_text(size=13), axis.text.x = element_text(size=13))+
          coord_cartesian(xlim = c(-2, 2), ylim=c(-4,4)) 
  #scale_y_continuous(breaks = c(-4,-3,-2,-1,0,1,2,3,4)) +
  #scale_x_continuous(breaks = c(-2,-1,0,1,2))

resid_cordplacenta_itu <- na.omit(Data_Cord_Placenta_ITU[ ,c("Sample_Name", "EAAR_Bohlin", "EAAR_Lee")])
resid_cordplacenta_itu$EAAR_Bohlin_s <- scale(resid_cordplacenta_itu$EAAR_Bohlin)
resid_cordplacenta_itu$EAAR_Lee_s <- scale(resid_cordplacenta_itu$EAAR_Lee)
names(resid_cordplacenta_itu) <- c("Sample_Name", "Cord blood", "Placenta", "EAAR Cord blood (scaled)", "EAAR Placenta (scaled)")
resid_cordplacenta_itu_ls = reshape2::melt(resid_cordplacenta_itu[ ,c(1:3)])
col_resid_cordplacenta_itu_ls <- factor(resid_cordplacenta_itu_ls$Sample_Name)

color_plot = grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), invert = T)]
color_plot <- color_plot[1:363]

box_cord_placenta_resid <- ggplot(data=resid_cordplacenta_itu_ls, aes(x=variable, y=value))+
  geom_boxplot()+
  #geom_point(aes(colour = col_resid_cordplacenta_itu_ls))+
  geom_jitter(aes(colour = col_resid_cordplacenta_itu_ls), size=0.4, alpha=0.9)+
  scale_color_manual(values=color_plot)+
  ylab("epigenetic age acceleration residuals")+ 
  xlab("")+
  theme(legend.position = "none") 

box_cord_placenta_resid

png(filename="Results/Figures/diffTissues/corEAAR_cord_placenta_ITU.png", width=2600, height=1600, res=500)
cor_cord_placenta_resid
dev.off()

png(filename="Results/Figures/diffTissues/corEAAR_cord_placenta_ITU_F.png", width=2600, height=1600, res=500)
cor_cord_placenta_resid_f
dev.off()

png(filename="Results/Figures/diffTissues/boxEAAR_cord_placenta_ITU.png", width=2800, height=1400, res=400)
box_cord_placenta_resid 
dev.off()

#levenes test 
leveneTest(value ~ variable, resid_cordplacenta_itu_ls, center=mean)
# significant
#Levene's Test for Homogeneity of Variance (center = mean)
#       Df F value    Pr(>F)    
#group   1  135.76 < 2.2e-16 ***
#      724
# paired t-test
d <- with(resid_cordplacenta_itu_ls, 
        value[variable == "Cord blood"] - value[variable == "Placenta"])
# Shapiro-Wilk normality test for the differences
shapiro.test(d)
# distribution of the differences (d) are not significantly different from normal distribution. In other words, we can assume the normality

t_paired_itu_resid <- t.test(value ~ variable, data = resid_cordplacenta_itu_ls, paired = TRUE)
t_paired_itu_resid
tidy_t_paired_itu_resid <- broom::tidy(t_paired_itu_resid)

ddply(resid_cordplacenta_itu_ls, .(variable), colwise(mean))
ddply(resid_cordplacenta_itu_ls, .(variable), colwise(sd))


write.csv(tidy_t_paired_itu_resid, "Results/Tables/t_paired_eaar_itu_cordplacenta.csv")

Cord blood and Placenta (in PREDO)

```r
DNAmGAResidCPREDO <- Data_PREDO_EPIC_Cord_Placenta[ ,c(\EAAR_Bohlin\,\EAAR_Lee\)]
colnames(DNAmGAResidCPREDO) <- c(\Cordblood\, \Placenta\)

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```r
allcorrsDNAmGAResidCPREDO <- rcorr(as.matrix(DNAmGAResidCPREDO))
allcorrsDNAmGAResidCPREDO
cor_cord_placenta_resid_predo <- ggscatter(Data_PREDO_EPIC_Cord_Placenta, x = "EAAR_Bohlin", y = "EAAR_Lee", 
          add = "reg.line", conf.int = TRUE, 
          xlab = "EAAR Cord blood", ylab = "EAAR decidual Placenta")+
          stat_cor(method = "pearson", label.x = -2, label.y = 4, r.digits = 1, p.digits = 2)+
          geom_hline(yintercept=0, linetype="dashed")+
          geom_vline(xintercept=0, linetype="dashed")+
        theme(text = element_text(size=13), axis.text.x = element_text(size=13))+
          coord_cartesian(xlim = c(-2, 2), ylim=c(-4,4))

cor_cord_placenta_resid_predo

cor_cord_placenta_resid_predo_f <- ggscatter(Data_PREDO_EPIC_Cord_Placenta, x = "EAAR_Bohlin", y = "EAAR_Lee", 
          add = "reg.line", conf.int = TRUE, 
          xlab = "EAAR Cord blood", ylab = "EAAR decidual Placenta")+
          geom_hline(yintercept=0, linetype="dashed")+
          geom_vline(xintercept=0, linetype="dashed")+
        theme(text = element_text(size=13), axis.text.x = element_text(size=13))+
          coord_cartesian(xlim = c(-2, 2), ylim=c(-4,4))

cor_cord_placenta_resid_predo_f

resid_cordplacenta_predo <- na.omit(Data_PREDO_EPIC_Cord_Placenta[ ,c("Sample_Name", "EAAR_Bohlin", "EAAR_Lee")])
resid_cordplacenta_predo$EAAR_Bohlin_s <- scale(resid_cordplacenta_predo$EAAR_Bohlin)
resid_cordplacenta_predo$EAAR_Lee_s <- scale(resid_cordplacenta_predo$EAAR_Lee)
names(resid_cordplacenta_predo) <- c("Sample_Name", "Cord blood", "Placenta", "EAAR Cord blood (scaled)", "EAAR Placenta (scaled)")
resid_cordplacenta_predo_ls = reshape2::melt(resid_cordplacenta_predo[ ,c(1:3)])
col_resid_cordplacenta_predo_ls <- factor(resid_cordplacenta_predo_ls$Sample_Name)

color_plot = grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), invert = T)]
color_plot <- color_plot[1:116]

box_cord_placenta_resid_predo <- ggplot(data=resid_cordplacenta_predo_ls, aes(x=variable, y=value))+
  geom_boxplot()+
  #geom_point(aes(colour = col_resid_cordplacenta_itu_ls))+
  geom_jitter(aes(colour = col_resid_cordplacenta_predo_ls), size=0.4, alpha=0.9)+
  scale_color_manual(values=color_plot)+
  ylab("epigenetic age acceleration residuals")+ 
  theme(legend.position = "none") 

box_cord_placenta_resid_predo

png(filename="Results/Figures/diffTissues/corEAAR_cord_placenta_PREDO.png", width=2600, height=1600, res=500)
cor_cord_placenta_resid_predo
dev.off()

png(filename="Results/Figures/diffTissues/corEAAR_cord_placenta_PREDO_F.png", width=2600, height=1600, res=500)
cor_cord_placenta_resid_predo_f
dev.off()

png(filename="Results/Figures/diffTissues/boxEAAR_cord_placenta_PREDO.png", width=2800, height=1400, res=400)
box_cord_placenta_resid_predo
dev.off()

#levenes test 
leveneTest(value ~ variable, resid_cordplacenta_predo_ls, center=mean)
# significant
# paired t-test
d <- with(resid_cordplacenta_predo_ls, 
        value[variable == "Cord blood"] - value[variable == "Placenta"])
# Shapiro-Wilk normality test for the differences
shapiro.test(d)
# distribution of the differences (d) are not significantly different from normal distribution. In other words, we can assume the normality

t_paired_predo_resid <- t.test(value ~ variable, data = resid_cordplacenta_predo_ls, paired = TRUE)
tidy_t_paired_predo_resid <- broom::tidy(t_paired_predo_resid)

write.csv(tidy_t_paired_predo_resid, "Results/Tables/t_paired_eaar_predo_cordplacenta.csv")

t_paired_predo_resid
ddply(resid_cordplacenta_predo_ls, .(variable), colwise(mean))
ddply(resid_cordplacenta_predo_ls, .(variable), colwise(sd))

CVS and Placenta

```r
DNAmGAResidCCP <- Data_CVS_Placenta_ITU[ ,c(\EAAR_Lee_CVS\, \EAAR_Lee_Placenta\)]
colnames(DNAmGAResidCCP) <- c(\CVS\, \Placenta\)

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```r
allcorrsDNAmGAResidCCP <- rcorr(as.matrix(DNAmGAResidCCP))
allcorrsDNAmGAResidCCP
cor_cvs_placenta_resid <- ggscatter(Data_CVS_Placenta_ITU, x = "EAAR_Lee_CVS", y = "EAAR_Lee_Placenta", 
          add = "reg.line", conf.int = TRUE, xlab = "EAAR CVS", ylab = "EAAR fetal Placenta")+
         theme(text = element_text(size=13), axis.text.x = element_text(size=13))+
         coord_cartesian(xlim = c(-2, 2), ylim=c(-4,4))+
          stat_cor(method = "pearson", label.x = -2, label.y = 4, r.digits = 2, p.digits = 2)+
          geom_hline(yintercept=0, linetype="dashed")+
          geom_vline(xintercept=0, linetype="dashed")

cor_cvs_placenta_resid

cor_cvs_placenta_resid_f <- ggscatter(Data_CVS_Placenta_ITU, x = "EAAR_Lee_CVS", y = "EAAR_Lee_Placenta", 
          add = "reg.line", conf.int = TRUE, xlab = "EAAR CVS", ylab = "EAAR fetal Placenta")+
          geom_hline(yintercept=0, linetype="dashed")+
          geom_vline(xintercept=0, linetype="dashed")+
        theme(text = element_text(size=13), axis.text.x = element_text(size=13))+
         coord_cartesian(xlim = c(-2, 2), ylim=c(-4,4))

cor_cvs_placenta_resid_f

resid_cvsplacenta_itu <- na.omit(Data_CVS_Placenta_ITU[ ,c("Sample_Name", "EAAR_Lee_CVS", "EAAR_Lee_Placenta")])
resid_cvsplacenta_itu$EAAR_Bohlin_s <- scale(resid_cvsplacenta_itu$EAAR_Lee_CVS)
resid_cvsplacenta_itu$EAAR_Lee_s <- scale(resid_cvsplacenta_itu$EAAR_Lee_Placenta)
names(resid_cvsplacenta_itu) <- c("Sample_Name", "CVS", "Placenta", "EAAR CVS (scaled)", "EAAR Placenta (scaled)")
resid_cvsplacenta_itu_ls = reshape2::melt(resid_cvsplacenta_itu[ ,c(1:3)])
col_resid_cvsplacenta_itu_ls <- factor(resid_cvsplacenta_itu_ls$Sample_Name)

color_plot = grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), invert = T)]
color_plot <- color_plot[1:78]

box_cvs_placenta_resid <- ggplot(data=resid_cvsplacenta_itu_ls, aes(x=variable, y=value))+
  geom_boxplot() +
  #geom_point(aes(colour = col_resid_cordplacenta_itu_ls))+
  geom_jitter(aes(colour = col_resid_cvsplacenta_itu_ls), size=0.4, alpha=0.9)+
  scale_color_manual(values=color_plot)+
  ylab("epigenetic age acceleration residuals")+ 
  xlab("")+
  theme(legend.position = "none") 

box_cvs_placenta_resid

png(filename="Results/Figures/diffTissues/corEAAR_cvs_placenta_ITU.png", width=2600, height=1600, res=500)
cor_cvs_placenta_resid
dev.off()

png(filename="Results/Figures/diffTissues/corEAAR_cvs_placenta_ITU_F.png", width=2600, height= 1600, res=500)
cor_cvs_placenta_resid_f
dev.off()

png(filename="Results/Figures/diffTissues/boxEAAR_cvs_placenta_ITU.png", width=2800, height=1400, res=400)
box_cvs_placenta_resid 
dev.off()

# test if variance in EAAR differes between cvs & placenta using levenes test
leveneTest(value ~ variable, resid_cvsplacenta_itu_ls, center=mean)
# not significant
# paired t-test
d <- with(resid_cvsplacenta_itu_ls, 
        value[variable == "CVS"] - value[variable == "Placenta"])
# Shapiro-Wilk normality test for the differences
shapiro.test(d)
# distribution of the differences (d) are significantly different from normal

t_paired_itu_cvsplacenta_resid <- t.test(value ~ variable, data = resid_cvsplacenta_itu_ls, paired = TRUE)
tidy_t_paired_itu_cvsplacenta_resid <- broom::tidy(t_paired_itu_cvsplacenta_resid)

t_paired_itu_cvsplacenta_resid

write.csv(tidy_t_paired_itu_cvsplacenta_resid, "Results/Tables/t_paired_itu_eaar_cvsplacenta.csv")

ddply(resid_cvsplacenta_itu_ls, .(variable), colwise(mean))
ddply(resid_cvsplacenta_itu_ls, .(variable), colwise(sd))

CVS and Cord blood

```r
DNAmGAResidCC <- Data_CVS_Cord_ITU[ ,c(\EAAR_Lee\, \EAAR_Bohlin\)]
colnames(DNAmGAResidCC) <- c(\CVS\, \Cord\)

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```r
allcorrsDNAmGAResidCC <- rcorr(as.matrix(DNAmGAResidCC))
allcorrsDNAmGAResidCC
cor_cvs_cord_resid <- ggscatter(Data_CVS_Cord_ITU, x = "EAAR_Lee", y = "EAAR_Bohlin", 
          add = "reg.line", conf.int = TRUE, xlab = "EAAR CVS", ylab = "EAAR Cord blood")+
          stat_cor(method = "pearson", label.x = -2, label.y = 2, r.digits = 2, p.digits = 2)+
          geom_hline(yintercept=0, linetype="dashed")+
          geom_vline(xintercept=0, linetype="dashed")+
  theme(text = element_text(size=13), axis.text.x = element_text(size=13))+
          coord_cartesian(xlim = c(-2, 2), ylim=c(-2,2))

cor_cvs_cord_resid

cor_cvs_cord_resid_f <- ggscatter(Data_CVS_Cord_ITU, x = "EAAR_Lee", y = "EAAR_Bohlin", 
          add = "reg.line", conf.int = TRUE, xlab = "EAAR CVS", ylab = "EAAR Cord blood")+
          geom_hline(yintercept=0, linetype="dashed")+
          geom_vline(xintercept=0, linetype="dashed")+
  theme(text = element_text(size=13), axis.text.x = element_text(size=13))+
          coord_cartesian(xlim = c(-2, 2), ylim=c(-2,2))

cor_cvs_cord_resid_f

resid_cvscord_itu <- na.omit(Data_CVS_Cord_ITU[ ,c("Sample_Name", "EAAR_Bohlin", "EAAR_Lee")])
resid_cvscord_itu$EAAR_Bohlin_s <- scale(resid_cvscord_itu$EAAR_Bohlin)
resid_cvscord_itu$EAAR_Lee_s <- scale(resid_cvscord_itu$EAAR_Lee)
names(resid_cvscord_itu) <- c("Sample_Name", "Cord blood", "CVS", "EAAR Cord blood (scaled)", "EAAR CVS (scaled)")
resid_cvscord_itu_ls = reshape2::melt(resid_cvscord_itu[ ,c(1:3)])
col_resid_cvscord_itu_ls <- factor(resid_cvscord_itu_ls$Sample_Name)

color_plot = grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), invert = T)]
color_plot <- color_plot[1:363]

box_cvs_cord_resid <- ggplot(data=resid_cvscord_itu_ls, aes(x=variable, y=value))+
  geom_boxplot()+
  #geom_point(aes(colour = col_resid_cordplacenta_itu_ls))+
  geom_jitter(aes(colour = col_resid_cvscord_itu_ls), size=0.4, alpha=0.9)+
  scale_color_manual(values=color_plot)+
  ylab("epigenetic age acceleration residuals")+ 
  xlab("")+
  theme(legend.position = "none") 

box_cvs_cord_resid

png(filename="Results/Figures/diffTissues/corEAAR_cvs_cord_ITU.png", width=2600, height=1600, res=500)
cor_cvs_cord_resid
dev.off()

png(filename="Results/Figures/diffTissues/corEAAR_cvs_cord_ITU_F.png", width=2600, height=1600, res=500)
cor_cvs_cord_resid_f
dev.off()

png(filename="Results/Figures/diffTissues/boxEAAR_cvs_cord_ITU.png", width=2800, height=1400, res=400)
box_cvs_cord_resid 
dev.off()

#levenes test 
leveneTest(value ~ variable, resid_cvscord_itu_ls, center=mean)
# significant
# Levene's Test for Homogeneity of Variance (center = mean)
#        Df F value    Pr(>F)    
# group   1   14.13 0.0002567 ***
#       130   
# paired t-test
d <- with(resid_cvscord_itu_ls, 
        value[variable == "CVS"] - value[variable == "Cord blood"])
# Shapiro-Wilk normality test for the differences
shapiro.test(d)
# distribution of the differences (d) are significantly different from normal

t_paired_itu_cvscord_resid <- t.test(value ~ variable, data = resid_cvscord_itu_ls, paired = TRUE)
tidy_t_paired_itu_cvscord_resid <- broom::tidy(t_paired_itu_cvscord_resid)

wilc_paired_itu_cvscord_resid <- wilcox.test(value ~ variable, data = resid_cvscord_itu_ls, paired = TRUE)
qnorm(wilc_paired_itu_cvscord_resid$p.value/2)
wilcoxonZ(resid_cvscord_itu$`Cord blood`, resid_cvscord_itu$CVS, paired = TRUE)
tidy_wilc_paired_itu_cvscord_resid <- broom::tidy(wilc_paired_itu_cvscord_resid)

write.csv(tidy_t_paired_itu_cvscord_resid, "Results/Tables/t_paired_itu_eaar_cvscord_resid.csv")
write.csv(tidy_wilc_paired_itu_cvscord_resid, "Results/Tables/wilc_paired_itu_eaar_cvscord_resid.csv")

wilc_paired_itu_cvscord_resid
ddply(resid_cvscord_itu_ls, .(variable), colwise(mean))
ddply(resid_cvscord_itu_ls, .(variable), colwise(sd))
png(filename="Results/Figures/diffTissues/EAAR_correlations_tissues.png", width=3000, height=2000, res=300)
gridExtra::grid.arrange(cor_cvs_placenta_resid, cor_cvs_cord_resid, cor_cord_placenta_resid, cor_cord_placenta_resid_predo, ncol = 2)
dev.off()

to the top

Difference in EAAR between Tissues

individuals with data from cordblood + placenta -ITU

```r
# difference between cordblood and placenta
Data_Cord_Placenta_ITU$differenceEAAR <- Data_Cord_Placenta_ITU$EAAR_Bohlin - Data_Cord_Placenta_ITU$EAAR_Lee
#n=390

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```r
```r
# What is the absolute difference between cordblood and placenta?
Data_Cord_Placenta_ITU$absdifferenceEAAR <- abs(Data_Cord_Placenta_ITU$EAAR_Bohlin - Data_Cord_Placenta_ITU$EAAR_Lee)

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```r
box_abs_resid_ITU <- ggplot(Data_Cord_Placenta_ITU, aes(x =Child_Sex, y = absdifferenceEAAR)) +
  geom_boxplot() +
  labs(x="child sex", y="absolute difference between EAARs", title="ITU")

melt_Data_Cord_Placenta_ITU <- reshape2::melt(Data_Cord_Placenta_ITU[ ,c("EAAR_Bohlin", "EAAR_Lee")])

box_EAAR_cordplacenta_ITU <- ggplot(melt_Data_Cord_Placenta_ITU, aes(x =factor(variable), y = value)) +
  geom_boxplot() +
  labs(x="", y="EAAR")+
  scale_x_discrete(labels = c('cord blood','placenta'))

hists_abs_resid_ITU <- ggplot(Data_Cord_Placenta_ITU, aes(x=absdifferenceEAAR))+ 
  geom_histogram(bins=58)+
  scale_x_continuous(breaks=c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5))+
  labs(x="absolute difference betweeen EAARs \n(cord blood vs. placenta)",y="Count (n = 363)")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


hists_resid_ITU <- ggplot(Data_Cord_Placenta_ITU, aes(x=differenceEAAR))+ 
  geom_histogram(bins=58)+
  scale_x_continuous(breaks=c(-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5))+
  labs(x="Cord blood - fetal Placenta (EAARs)", y = "Count (n = 363)")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


grid.arrange(box_abs_resid_ITU, hists_abs_resid_ITU, ncol=2)

median(Data_Cord_Placenta_ITU$absdifferenceEAAR, na.rm=T)

box_EAAR_cordplacenta_ITU

hists_resid_ITU

individuals with data from cord blood and placenta - PREDO

```r
# difference between cordblood and placenta
Data_PREDO_EPIC_Cord_Placenta$differenceEAAR <- Data_PREDO_EPIC_Cord_Placenta$EAAR_Bohlin - Data_PREDO_EPIC_Cord_Placenta$EAAR_Lee

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<div class="alert alert-info">
* variable differenceresidualGAC = residual GA for cordblood minus residual GA for placenta (residual from DNAmGA~GA)    
</div>


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```r
```r
# What is the absolute difference between cordblood and placenta?
Data_PREDO_EPIC_Cord_Placenta$absdifferenceEAAR <- abs(Data_PREDO_EPIC_Cord_Placenta$EAAR_Bohlin - Data_PREDO_EPIC_Cord_Placenta$EAAR_Lee)

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<div class="alert alert-info">
* variable absdifferenceresidualGAC = absolute difference between residual GA for cordblood vs placenta
</div>


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```r
box_abs_resid_PREDO <- ggplot(Data_PREDO_EPIC_Cord_Placenta, aes(x =Child_Sex, y = absdifferenceEAAR)) +
  geom_boxplot() +
  labs(x="child sex", y="absolute difference between EAARs", title="PREDO")

hists_abs_resid_PREDO <- ggplot(Data_PREDO_EPIC_Cord_Placenta, aes(x=absdifferenceEAAR))+ 
  geom_histogram(bins=58)+
  scale_x_continuous(breaks=c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5))+
  labs(x="absolute difference betweeen EAARs \n(cord blood vs. placenta)",y="Count (n = 116)")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


grid.arrange(box_abs_resid_PREDO, hists_abs_resid_PREDO, ncol=2)

median(Data_PREDO_EPIC_Cord_Placenta$absdifferenceEAAR, na.rm=T)

hists_resid_PREDO <- ggplot(Data_PREDO_EPIC_Cord_Placenta, aes(x=differenceEAAR))+ 
  geom_histogram(bins=58)+
  scale_x_continuous(breaks=c(-3,-2,-1,0,1,2,3,4))+
  labs(x="Cord blood - decidual Placenta (EAARs)", y="Count (n = 116)")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

  

hists_resid_PREDO

individuals with data from cvs + cordblood

```r
# difference between cvs and cordblood 
Data_CVS_Cord_ITU$differenceEAAR <- Data_CVS_Cord_ITU$EAAR_Lee - Data_CVS_Cord_ITU$EAAR_Bohlin
#n=73

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```r
```r
# What is the absolute difference between cordblood and placenta?
Data_CVS_Cord_ITU$absdifferenceEAAR <- abs(Data_CVS_Cord_ITU$EAAR_Lee - Data_CVS_Cord_ITU$EAAR_Bohlin)

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```r
box_abs_resid_ITU_cc <- ggplot(Data_CVS_Cord_ITU, aes(x =Child_Sex, y = absdifferenceEAAR)) +
  geom_boxplot() +
  labs(x="child sex", y="absolute difference between EAARs", title="ITU")

melt_Data_CVS_Cord_ITU <- reshape2::melt(Data_CVS_Cord_ITU[ ,c("EAAR_Bohlin", "EAAR_Lee")])

box_EAAR_cvscord_ITU <- ggplot(melt_Data_CVS_Cord_ITU, aes(x =factor(variable), y = value)) +
  geom_boxplot() +
  labs(x="", y="EAAR")
  #scale_x_discrete(labels = c('cord blood','placenta'))

hists_abs_resid_ITU_cc <- ggplot(Data_CVS_Cord_ITU, aes(x=absdifferenceEAAR))+ 
  geom_histogram(bins=58)+
  labs(x="absolute difference betweeen EAARs \n(cord blood vs. placenta)",y="Count (n = 66)")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


hists_resid_ITU_cc <- ggplot(Data_CVS_Cord_ITU, aes(x=differenceEAAR))+ 
  geom_histogram(bins=58)+
  coord_cartesian(xlim = c(-4, 4))+
  #scale_x_continuous(limits = c(-4, 4))+
  scale_x_continuous(breaks=c(-4,-3, -2, -1, 0, 1, 2, 3,4))+
  labs(x="CVS - Cord blood (EAARs)", y = "Count (n = 66)")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


grid.arrange(box_abs_resid_ITU_cc, hists_abs_resid_ITU_cc, ncol=2)

median(Data_CVS_Cord_ITU$absdifferenceEAAR, na.rm=T)

box_EAAR_cvscord_ITU

hists_resid_ITU_cc

individuals with data from cvs + placenta

```r
# difference between cvs and placenta 
Data_CVS_Placenta_ITU$differenceEAAR <- Data_CVS_Placenta_ITU$EAAR_Lee_CVS - Data_CVS_Placenta_ITU$EAAR_Lee_Placenta
#n=86

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```r
```r
# What is the absolute difference between cordblood and placenta?
Data_CVS_Placenta_ITU$absdifferenceEAAR <- abs(Data_CVS_Placenta_ITU$EAAR_Lee_CVS - Data_CVS_Placenta_ITU$EAAR_Lee_Placenta)

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```r
box_abs_resid_ITU_cp <- ggplot(Data_CVS_Placenta_ITU, aes(x =Child_Sex, y = absdifferenceEAAR)) +
  geom_boxplot() +
  labs(x="child sex", y="absolute difference between EAARs", title="ITU")

melt_Data_CVS_Placenta_ITU <- reshape2::melt(Data_CVS_Placenta_ITU[ ,c("EAAR_Lee_CVS", "EAAR_Lee_Placenta")])

box_EAAR_cvsplacenta_ITU <- ggplot(melt_Data_CVS_Placenta_ITU, aes(x =factor(variable), y = value)) +
  geom_boxplot() +
  labs(x="", y="EAAR")
  #scale_x_discrete(labels = c('cord blood','placenta'))

hists_abs_resid_ITU_cp <- ggplot(Data_CVS_Placenta_ITU, aes(x=absdifferenceEAAR))+ 
  geom_histogram(bins=58)+
  scale_x_continuous(breaks=c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5))+
  labs(x="absolute difference betweeen EAARs \n(cord blood vs. placenta)",y="Count (n = 78)")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


hists_resid_ITU_cp <- ggplot(Data_CVS_Placenta_ITU, aes(x=differenceEAAR))+ 
  geom_histogram(bins=58)+
  coord_cartesian(xlim = c(-3, 3))+
  #scale_x_continuous(limits = c(-4, 4))+
  scale_x_continuous(breaks=c(-3, -2, -1, 0, 1, 2, 3))+
  labs(x="CVS - fetal Placenta (EAARs)", y = "Count (n = 78)")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


grid.arrange(box_abs_resid_ITU_cp, hists_abs_resid_ITU_cp, ncol=2)

median(Data_CVS_Placenta_ITU$absdifferenceEAAR, na.rm=T)

box_EAAR_cvsplacenta_ITU

hists_resid_ITU_cp

individuals with data from cvs + cordblood + placenta

```r
resid_Data_Full_ITU <- na.omit(Data_Full_ITU[ ,c(\Sample_Name\, \EAAR_Bohlin\, \EAAR_Lee_CVS\, \EAAR_Lee_Placenta\)]) #60
resid_Data_Full_ITU_z <- resid_Data_Full_ITU[ ,c(\Sample_Name\, \EAAR_Bohlin\, \EAAR_Lee_CVS\, \EAAR_Lee_Placenta\)]

resid_Data_Full_ITU$`Cord blood` <- resid_Data_Full_ITU$EAAR_Bohlin
resid_Data_Full_ITU$CVS <- resid_Data_Full_ITU$EAAR_Lee_CVS
resid_Data_Full_ITU$`Placenta (fetal)` <- resid_Data_Full_ITU$EAAR_Lee_Placenta
resid_Data_Full_ITU$EAAR_Bohlin <- NULL
resid_Data_Full_ITU$EAAR_Lee_CVS <- NULL
resid_Data_Full_ITU$EAAR_Lee_Placenta <- NULL

resid_Data_Full_ITU_z$`Cord blood` <- scale(resid_Data_Full_ITU_z$EAAR_Bohlin)
resid_Data_Full_ITU_z$CVS <- scale(resid_Data_Full_ITU_z$EAAR_Lee_CVS)
resid_Data_Full_ITU_z$`Placenta (fetal)` <- scale(resid_Data_Full_ITU_z$EAAR_Lee_Placenta)
resid_Data_Full_ITU_z$EAAR_Bohlin <- NULL
resid_Data_Full_ITU_z$EAAR_Lee_CVS <- NULL
resid_Data_Full_ITU_z$EAAR_Lee_Placenta <- NULL

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```r
```r
long_resid_Data_Full_ITU_z <- melt(as.data.table(resid_Data_Full_ITU_z), id.vars = \Sample_Name\, variable.name = \sampling\)
long_resid_Data_Full_ITU_z$sampling <- factor(long_resid_Data_Full_ITU_z$sampling, levels = c(\CVS\, \Placenta (fetal)\, \Cord blood\))

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```r
```r
long_resid_Data_Full_ITU <- melt(as.data.table(resid_Data_Full_ITU), id.vars = \Sample_Name\, variable.name = \sampling\)
long_resid_Data_Full_ITU$sampling <- factor(long_resid_Data_Full_ITU$sampling, levels = c(\CVS\, \Placenta (fetal)\, \Cord blood\))

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```r
```r
library(randomcoloR)
n <- 60
palette <- distinctColorPalette(n)

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*Plots*

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```r
ggplot(long_resid_Data_Full_ITU_z, aes(x=sampling, y=value, group=as.factor(Sample_Name), color=as.factor(Sample_Name))) + 
  geom_point()+
  geom_line()+
  scale_color_manual(values=palette)+
  theme_bw()+
  theme(legend.position = "none")+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))+
  labs(x="", y = "z-standardized EAAR")
ggplot(long_resid_Data_Full_ITU, aes(x=sampling, y=value, group=as.factor(Sample_Name), color=as.factor(Sample_Name))) + 
  geom_point()+
  geom_line()+
  scale_color_manual(values=palette)+
  theme_bw()+
  theme(legend.position = "none")+
  theme(text = element_text(size = 15, color="black"), axis.title.x= element_text(size=15, color="black"), axis.title.y= element_text(size=15), axis.text.x=element_text(colour="black"))+
  labs(x="", y = "EAAR (n = 60)")
png(file="Results/Figures/diffTissues/EAAR_CVSCordPlacenta_ITU.png", width=3000, height=1500, res=400)
ggplot(long_resid_Data_Full_ITU, aes(x=sampling, y=value, group=as.factor(Sample_Name), color=as.factor(Sample_Name))) + 
  geom_point()+
  geom_line()+
  scale_color_manual(values=palette)+
  theme_bw()+
  theme(legend.position = "none")+
  theme(text = element_text(size = 11, color="black"), axis.title.x= element_text(size=13, color="black"), axis.title.y= element_text(size=13), axis.text.x=element_text(size=13, colour="black"))+
  labs(x="", y = "EAAR (n = 60)")
dev.off()
png(file="Results/Figures/diffTissues/EAAR_PlacentaCord_ITU.png", width=2500, height=1500, res=400)
hists_abs_resid_ITU
dev.off()

png(file="Results/Figures/diffTissues/EAAR_PlacentaCord_PREDO.png", width=2500, height=1500, res=400)
hists_abs_resid_PREDO
dev.off()

png(file="Results/Figures/diffTissues/EAAR_PlacentaCord.png", width=3500, height=1500, res=400)
grid.arrange(hists_abs_resid_ITU, hists_abs_resid_PREDO, ncol = 2)
dev.off()

png(file="Results/Figures/diffTissues/EAAR_diffCordPlacenta_ITU.png", width=2500, height=1500, res=400)
hists_resid_ITU
dev.off()

png(file="Results/Figures/diffTissues/EAAR_diffCordPlacenta_PREDO.png", width=2500, height=1500, res=400)
hists_resid_PREDO
dev.off()

png(file="Results/Figures/diffTissues/EAAR_diffCVSCord_ITU.png", width=2500, height=1500, res=400)
hists_resid_ITU_cc
dev.off()

png(file="Results/Figures/diffTissues/EAAR_diffPlacentaCVS_ITU.png", width=2500, height=1500, res=400)
hists_resid_ITU_cp
dev.off()

to the top

---
title: "characteristics of epigenetic aging across gestational/perinatal tissues"
author: "Linda Dieckmann"
date: "March 2021"
output:
   github_document:
   html_notebook:
    toc: yes
    toc_float: yes
    collapsed: no
    theme: flatly
  
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(results = "hide", message=FALSE, eval=FALSE)
```

***
##### **Jump to sections** {#top}  
***

**General**  
[start with loading packages & functions](#loadpacks)  
[loading data & Sample Overview](#loadData)  

Prepare data for elastic net models:  
[CVS: data preparation for models](#dataprepCVSITU)  
[Cord blood: data preparation](#dataprepCordITU)  
[Placenta: data preparation](#dataprepPlacentaITU)  
[Cord blood EPIC: data preparation](#dataprepCordPREDO)   
[Cord blood 450K: data preparation](#dataprepCord450KPREDO)  
[Placenta: data preparation](#dataprepPlacentaPREDO)  

[SampleVisualization](#Samples)  

**Basics & Descriptive Statistics:**  
[ITU](#ITUDescriptive)  
[PREDO](#PREDODescriptive)  

[comparison PREDO & ITU in predictors](#predictorsITUPREDO)  

[ITU: look at predictors (& correlations), in full data (all persons)](#PredictorsITUAll)  
[PREDO: look at predictors (& correlations), in full data (all persons)](#PredictorsPREDOAll)  

[ITU: correlation DNAmGA & GA for clocks](#corDNAmGAGAITU)  
[PREDO: correlation DNAmGA & GA for clocks](#corDNAmGAGAPREDO)  
[Plots correlation DNAmGA & GA](#PlotcorDNAmGAGA)  

[Clocks: correlation cord blood clocks](#corCordClocks)  
[Clocks: correlation placenta clocks](#corPlacentaClocks)  

[ITU: Visualization EAAR](#plotsEAARITU)  
[PREDO: Visualization EAAR](#plotsEAARPREDO)  

**Single Tissues Analyses**  
*ITU*  
[Cord blood: elastic net main](#elasticnetCVSITU)  
[Cord blood: elastic net including maternal alcohol use](#elasticnetCordITU_a)  
[CVS: elastic net main](#elasticnetCVSITU)  
[CVS: elastic net including maternal alcohol use](#elasticnetCVSITU_a)  
[Placenta: elastic net main](#elasticnetPlacentaITU)  
[Placenta: elastic net including maternal alcohol use](#elasticnetPlacentaITU_a)  
[Placenta: elastic net splitted by sex](#elasticnetPlacentaITU_s)  
*PREDO*  
[Placenta: elastic net main](#elasticnetPlacentaPREDO)  
[Placenta: elastic net including maternal alcohol use](#elasticnetPlacentaPREDO_a)  
[Placenta: elastic net splitted by sex](#elasticnetPlacentaPREDO_s)  
[Cord blood: prediction](#predictionCordPREDO)  
[Cord blood: elastic net main](#elasticnetCordPREDO)  
[Cord blood: elastic net main](#elasticnetCordPREDO450)  

**Cross-Tissue Analyses**  
[DNAmGA between tissues](#corTissuesDNAmGA)  
[EAAR between tissues](#corTissuesEAAR)  
[Difference in EAAR between Tissues](#DifferenceEAARTissues)  


***

# load packages {#loadpacks}  


<!-- ```{r} -->
<!-- # critical to do this whenever using new R/computer (otherwise problems with replicate, bootstrapping): -->

<!-- if ( !("ensr" %in% rownames(installed.packages())) || (packageVersion("ensr") < "0.1.0.9001" ) ) { -->
<!--   remotes::install_github('dewittpe/ensr') -->
<!-- } -->
<!-- ``` -->

<!-- ```{r} -->
<!-- install.packages("psych") -->
<!-- install.packages("corrplot") -->
<!-- install.packages("Rmisc") -->
<!-- install.packages("pastecs") -->
<!-- install.packages("reshape2") -->
<!-- install.packages("ggpubr") -->
<!-- install.packages("ggplot2") -->
<!-- install.packages("Hmisc") -->
<!-- install.packages("cowplot") -->
<!-- install.packages("tibble") -->
<!-- install.packages("dplyr") -->
<!-- install.packages("gridExtra") -->
<!-- install.packages("car") -->
<!-- install.packages("tidyverse") -->
<!-- #install.packages("caret") -->
<!-- install.packages("leaps") -->
<!-- #install.packages("AppliedPredictiveModeling") -->
<!-- install.packages("pairsD3") -->
<!-- install.packages("rgl") -->
<!-- install.packages("plot3D") -->
<!-- install.packages("plot3Drgl") -->
<!-- install.packages("glmnet") -->
<!-- install.packages("MASS") -->
<!-- install.packages("sjPlot") -->
<!-- install.packages("sjlabelled") -->
<!-- install.packages("skimr") -->
<!-- install.packages("ggforce") -->
<!-- install.packages("doMC", repos="http://R-Forge.R-project.org") -->
<!-- install.packages("qwraps2") -->
<!-- install.packages("knitr") -->
<!-- install.packages("magrittr") -->
<!-- install.packages("remotes") -->
<!-- install.packages("qwraps2") -->
<!-- install.packages("rmarkdown") -->
<!-- install.packages("data.table") -->
<!-- install.packages("qgraph") -->
<!-- install.packages("miscTools") -->
<!-- install.packages("akmedoids") -->
<!-- install.packages("stargazer") -->
<!-- install.packages("ppcor") -->
<!-- install.packages("IsingSampler") -->
<!-- ``` -->


```{r,include=FALSE}
#options(scipen=999)
#options(scipen=0)

library(psych)
library(corrplot)
library(Rmisc)
library(pastecs)
library(reshape2)
library(ggpubr)
library(ggplot2)
library(Hmisc)
library(cowplot)
library(tibble)
library(dplyr)
library(gridExtra)
library(car)
library(tidyverse)
library(leaps)
library(pairsD3)
library(rgl)
library(plot3D)
library(plot3Drgl)
library(glmnet)
library(MASS)
library(sjPlot)
library(sjlabelled)
library(skimr)
library(ensr)
library(ggforce)
library(doMC)
library(qwraps2)
library(knitr)
library(magrittr)
library(remotes)
library(qwraps2)
library(rmarkdown)
library(data.table)
library(qgraph)
library(miscTools)
library(akmedoids)
library(stargazer)
library(ppcor)
library(IsingSampler)
library(writexl)
library(eulerr)
library(VennDiagram)
library(Hotelling)
library(rrcov)
library(rcompanion)
```

```{r}
# outlier function for descriptive graphs
is_outlier <- function(x) {
  return(x < quantile(x, 0.25) - 1.5 * IQR(x) | x > quantile(x, 0.75) + 1.5 * IQR(x))}

# elbow finder for number of nzero coefficients
elbow_finder <- function(x_values, y_values) {
  # Max values to create line
  max_x_x <- max(x_values)
  max_x_y <- y_values[which.max(x_values)]
  max_y_y <- max(y_values)
  max_y_x <- x_values[which.max(y_values)]
  max_df <- data.frame(x = c(max_y_x, max_x_x), y = c(max_y_y, max_x_y))
  
  # Creating straight line between the max values
  fit <- lm(max_df$y ~ max_df$x)
  
  # Distance from point to line
  distances <- c()
  for(i in 1:length(x_values)) {
    distances <- c(distances, abs(coef(fit)[2]*x_values[i] - y_values[i] + coef(fit)[1]) / sqrt(coef(fit)[2]^2 + 1^2))
  }
  
  # Max distance point
  x_max_dist <- x_values[which.max(distances)]
  y_max_dist <- y_values[which.max(distances)]
  
  return(c(x_max_dist, y_max_dist))
}
```


```{r}
options(scipen=999)
```

```{r}
writeLines(capture.output(sessionInfo()), "sessionInfo.txt")
```

# Load saved data to start from here {#loadData}  
Note that the working directory is the directory where the Script is located

Here I provide the prepared Data:
```{r}
load(file= "InputData/ClockCalculationsInput/Data_CVS_ITU.Rdata")
load(file= "InputData/ClockCalculationsInput/Data_Cord_ITU.Rdata")
load(file= "InputData/ClockCalculationsInput/Data_Placenta_ITU.Rdata")
load(file="InputData/ClockCalculationsInput/Data_Full_ITU.Rdata") # data persons with all measurement points available
load(file="InputData/ClockCalculationsInput/Data_Cord_Placenta_ITU.Rdata")
load(file="InputData/ClockCalculationsInput/Data_CVS_Placenta_ITU.Rdata")
load(file="InputData/ClockCalculationsInput/Data_CVS_Cord_ITU.Rdata")
load(file="InputData/ClockCalculationsInput/Data_ITU_all.Rdata") # all persons together in one data frame

load(file= "InputData/ClockCalculationsInput/Data_Placenta_male_ITU.Rdata")
load(file= "InputData/ClockCalculationsInput/Data_Placenta_female_ITU.Rdata")

load(file="InputData/ClockCalculationsInput/Data_PREDO_450Kcord.Rdata")
load(file="InputData/ClockCalculationsInput/Data_PREDO_EPICcord.Rdata")
load(file="InputData/ClockCalculationsInput/Data_PREDO_EPICplacenta.Rdata")
load(file="InputData/ClockCalculationsInput/Data_PREDO_EPIC_Cord_Placenta.Rdata")
load(file="InputData/ClockCalculationsInput/Data_PREDO_EPIC_all.Rdata") # all persons with EPIC data together in one data frame

load(file="InputData/ClockCalculationsInput/Data_PREDO_Placenta_male.Rdata")
load(file="InputData/ClockCalculationsInput/Data_PREDO_Placenta_female.Rdata")
```



<div class="alert alert-info">
  <strong>This is how I calculated measures of age acceleration/deceleration:</strong>  
  
* **EAAR <- as.numeric(residuals(lm(DNAmGA_Lee ~ Gestational_Age_Weeks + Trophoblasts + Stromal + Hofbauer + Endothelial + nRBC + Syncytiotrophoblast + PC1_ethnicity + PC2_ethnicity, data=X, na.action=na.exclude)))**  
= a positive value means acceleration, a negative value deceleration 
</div>


**Sample overview**
![ ](Sample_Overview/sample_ITU.PNG)
![ ](Sample_Overview/sample_ITU_2.PNG)
![ ](Sample_Overview/sample_PREDO.PNG)
![ ](Sample_Overview/sample_PREDO_2.PNG)

[to the top](#top)

**General Comments**

<strong>note on the influence of missing CpGs:</strong>  
  
* for the clock of placenta (Lee): not all CpGs included in the clock would have been included *after* our QC, however they were used here because they are needed for the clock (discussed with Steve Horvath).    
* for the clock of placenta (Mayne): not all CpGs of the clock are available, because the clock was again trained on 450K/27K data. Although the authors here did not report the comparability between the reduced and full clock, we excluded the 5 missing CpGs (that are in the clock, but not in our data) and predicted age.  
* for the clock of Bohlin et al. (cordblood), 8 CpGs are missing in the EPIC data (clock designed on Illumina 450K/27K/CHARM data). Again, the authors did not report a correlation between a reduced and full clock.  
* for the clock of Knight et al. (cordblood), 6 CpGs are missing in the EPIC data, because the clock was designed on Illumina 450K/27K data. Here, Knight et al. claimed that the clock would work anyways (tested correlation between estimates from reduced predictor and full predictor).
  
* the correlation between the estimated DNAmGA of the full and reduced **Bohlin clock** is r= .99 p < 2.2e-16 (tested with PREDO 450K)    
* the mean of the weights of the missing CpGS of the Bohlin clock is -2.159  
* the *reported* correlation between the estimated DNAmGA of the full and reduced **Knight clock** is r=.995  
* in our data the correlation is r=.97 p < 2.2e-16  
* the estimation from the reduced clock is again on average higher than the estimation from the full clock  
* the mean of the weights of the missing CpGS of the Bohlin clock is -0.767  
-> overall, both the reduced and full clock come to quite similar results, but the mean DNAm GA estimate differs (account for by using residuals)  

McEwen et al. (2018) tested if the 19 CpGs from the Horvath and the 6 CpGs from the Hannum Clock missing on the EPIC array have a great impact on the performance of the Clocks. They had data from both 450K and EPIC. Additionally, they tested the influence of different preprocessing strategies.  

https://pubmed.ncbi.nlm.nih.gov/30326963/

Dhingra et al. (2019) also evaluated the influence of missing CpGs of the Horvath clock by comparing 450K/EPIC data.  

https://pubmed.ncbi.nlm.nih.gov/31002714/

In summary, it is better to use age-adjusted residuals as a measure of age acceleration/deceleration, compared to the raw difference between estimated and chronological age.


### Data Preparation
## CVS, data preparation for models {#dataprepCVSITU}  

*regression input*
```{r}
# EAAR, without alcohol
Reg_Input_Data_CVS_ITU_EAAR_n <- Data_CVS_ITU[, c("EAAR_Lee", "Child_Sex", "Gestational_Age_Weeks", "Maternal_Age_Years", "smoking_dichotom", "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders")]

# EAAR, with alcohol
Reg_Input_Data_CVS_ITU_EAAR_wa <- Data_CVS_ITU[, c("EAAR_Lee", "Child_Sex", "Gestational_Age_Weeks", "Maternal_Age_Years", "smoking_dichotom", "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders", "maternal_alcohol_use")]

```

```{r}
sapply(Reg_Input_Data_CVS_ITU_EAAR_n, function(x) sum(is.na(x)))
sapply(Reg_Input_Data_CVS_ITU_EAAR_wa, function(x) sum(is.na(x)))
```

data frame without missings

```{r}
Reg_Input_Data_CVS_ITU_EAAR_n_noNa <- na.omit(Reg_Input_Data_CVS_ITU_EAAR_n) 
dim(Reg_Input_Data_CVS_ITU_EAAR_n_noNa)
```

```{r}
Reg_Input_Data_CVS_ITU_EAAR_wa_noNa <- na.omit(Reg_Input_Data_CVS_ITU_EAAR_wa) 
dim(Reg_Input_Data_CVS_ITU_EAAR_wa_noNa)
```

```{r}
skimr::skim(Reg_Input_Data_CVS_ITU_EAAR_n_noNa)
```


```{r}
save(Reg_Input_Data_CVS_ITU_EAAR_n_noNa, file="InputData/ClockCalculationsInput/Reg_Input_Data_CVS_ITU_EAAR_n_noNa.Rdata")
save(Reg_Input_Data_CVS_ITU_EAAR_wa_noNa, file="InputData/ClockCalculationsInput/Reg_Input_Data_CVS_ITU_EAAR_wa_noNa.Rdata")
```

[to the top](#top) 

## Cord blood, data preparation for models {#dataprepCordITU}

*regression input*

```{r}
# EAAR without alcohol
Reg_Input_Data_Cord_ITU_EAAR_n <- Data_Cord_ITU[, c("EAAR_Bohlin", "Child_Sex", "Maternal_Age_Years", "smoking_dichotom", "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders")]

# EAAR with alcohol
Reg_Input_Data_Cord_ITU_EAAR_wa <- Data_Cord_ITU[, c("EAAR_Bohlin", "Child_Sex", "Maternal_Age_Years", "smoking_dichotom",  "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders", "maternal_alcohol_use")]

```

```{r}
sapply(Data_Cord_ITU, function(x) sum(is.na(x)))
```

data frame without missings
```{r}
Reg_Input_Data_Cord_ITU_EAAR_noNa_n <- na.omit(Reg_Input_Data_Cord_ITU_EAAR_n) 
dim(Reg_Input_Data_Cord_ITU_EAAR_noNa_n)
Reg_Input_Data_Cord_ITU_EAAR_noNa_wa <- na.omit(Reg_Input_Data_Cord_ITU_EAAR_wa) 
dim(Reg_Input_Data_Cord_ITU_EAAR_noNa_wa)
```

```{r}
skimr::skim(Reg_Input_Data_Cord_ITU_EAAR_noNa_n)
```


```{r}
save(Reg_Input_Data_Cord_ITU_EAAR_noNa_wa, file="InputData/ClockCalculationsInput/Reg_Input_Data_Cord_ITU_EAAR_noNa_wa.Rdata")
save(Reg_Input_Data_Cord_ITU_EAAR_noNa_n, file="InputData/ClockCalculationsInput/Reg_Input_Data_Cord_ITU_EAAR_noNa_n.Rdata")
```

[to the top](#top)

## Placenta, data preparation for model {#dataprepPlacentaITU}  

*regression input*
```{r}
# without alcohol
Reg_Input_Data_Placenta_ITU_EAAR_n <- Data_Placenta_ITU[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_Years", "smoking_dichotom",  "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders")]

# with alcohol
Reg_Input_Data_Placenta_ITU_EAAR_wa <- Data_Placenta_ITU[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_Years", "smoking_dichotom",  "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders", "maternal_alcohol_use")]
```

```{r}
# for split by sex
# with alcohol
Reg_Input_Data_Placenta_male_ITU_EAAR_wa <- Data_Placenta_male_ITU[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_Years", "smoking_dichotom",  "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders", "maternal_alcohol_use")]

# without alcohol
Reg_Input_Data_Placenta_male_ITU_EAAR_n <- Data_Placenta_male_ITU[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_Years", "smoking_dichotom",  "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders")]


# with alcohol
Reg_Input_Data_Placenta_female_ITU_EAAR_wa <- Data_Placenta_female_ITU[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_Years", "smoking_dichotom",  "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders", "maternal_alcohol_use")]

# without alcohol
Reg_Input_Data_Placenta_female_ITU_EAAR_n <- Data_Placenta_female_ITU[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_Years", "smoking_dichotom",  "Delivery_mode_dichotom", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight","Child_Birth_Length", "Child_Head_Circumference_At_Birth","Parity_dichotom", "Induced_Labour", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders")]

```


```{r}
sapply(Data_Placenta_ITU, function(x) sum(is.na(x)))
```


data frame without missings
```{r}
Reg_Input_Data_Placenta_ITU_EAAR_noNa_n <- na.omit(Reg_Input_Data_Placenta_ITU_EAAR_n) 
dim(Reg_Input_Data_Placenta_ITU_EAAR_noNa_n)

Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa <- na.omit(Reg_Input_Data_Placenta_ITU_EAAR_wa) 
dim(Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa)
```

```{r}
# for split by sex
Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_wa <- na.omit(Reg_Input_Data_Placenta_male_ITU_EAAR_wa) 
dim(Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_wa)

Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_n <- na.omit(Reg_Input_Data_Placenta_male_ITU_EAAR_n) 
dim(Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_n)


Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_wa <- na.omit(Reg_Input_Data_Placenta_female_ITU_EAAR_wa) 
dim(Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_wa)

Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_n <- na.omit(Reg_Input_Data_Placenta_female_ITU_EAAR_n) 
dim(Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_n)

```

```{r}
skimr::skim(Reg_Input_Data_Placenta_ITU_EAAR_noNa_n)
```


```{r}
save(Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa.Rdata")

save(Reg_Input_Data_Placenta_ITU_EAAR_noNa_n, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_ITU_EAAR_noNa_n.Rdata")

```


```{r}
save(Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_wa, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_wa.Rdata")
save(Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_n, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_n.Rdata")

save(Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_wa, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_wa.Rdata")
save(Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_n, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_n.Rdata")
```


[to the top](#top) 


## cord blood data preparation for model {#dataprepCordPREDO}  
*EPIC*

*regression input*

```{r}
# EAAR without alcohol
Reg_Input_Data_Cordblood_PREDO_EAAR_n <- Data_PREDO_EPICcord[, c("EAAR_Bohlin", "Child_Sex", "Gestational_Age", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]

# EAAR with alcohol
Reg_Input_Data_Cordblood_PREDO_EAAR_wa <- Data_PREDO_EPICcord[, c("EAAR_Bohlin", "Child_Sex", "Gestational_Age", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Alcohol_Use_In_Early_Pregnancy_19Oct", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]
```


data frame without missings
```{r}
Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n <- na.omit(Reg_Input_Data_Cordblood_PREDO_EAAR_n) 
dim(Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n)

Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_wa <- na.omit(Reg_Input_Data_Cordblood_PREDO_EAAR_wa) 
dim(Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_wa)
```

```{r}
skimr::skim(Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n)
```


```{r}
save(Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_wa, file="InputData/ClockCalculationsInput/Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_wa.Rdata")

save(Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n, file="InputData/ClockCalculationsInput/Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n.Rdata")
```

[to the top](#top) 


## cord blood data preparation for model {#dataprepCord450KPREDO}  
*450K*

*regression input*

```{r}
# EAAR without alcohol
Reg_Input_Data_Cordblood_PREDO450K_EAAR_n <- Data_PREDO_450Kcord[, c("EAAR_Bohlin", "Child_Sex", "Gestational_Age", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]

#EAAR with alcohol
Reg_Input_Data_Cordblood_PREDO450K_EAAR_wa <- Data_PREDO_450Kcord[, c("EAAR_Bohlin", "Child_Sex", "Gestational_Age", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Alcohol_Use_In_Early_Pregnancy_19Oct", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]
```


```{r}
sapply(Reg_Input_Data_Cordblood_PREDO450K_EAAR_wa, function(x) sum(is.na(x)))
```


data frame without missings
```{r}
Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_wa <- na.omit(Reg_Input_Data_Cordblood_PREDO450K_EAAR_wa) 
dim(Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_wa)

Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n <- na.omit(Reg_Input_Data_Cordblood_PREDO450K_EAAR_n) 
dim(Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n)
```

```{r}
skimr::skim(Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n)
```


```{r}
save(Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_wa, file="InputData/ClockCalculationsInput/Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_wa.Rdata")

save(Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n, file="InputData/ClockCalculationsInput/Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n.Rdata")
```

[to the top](#top) 

## placenta: data preparation for model {#dataprepPlacentaPREDO}  
*Placenta EPIC*

*regression input*

```{r}
# EAAR (with ethnicity) without alcohol
Reg_Input_Data_Placenta_PREDO_EAAR_n <- Data_PREDO_EPICplacenta[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]

# EAAR (with ethnicity) with alcohol
Reg_Input_Data_Placenta_PREDO_EAAR_wa <- Data_PREDO_EPICplacenta[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Alcohol_Use_In_Early_Pregnancy_19Oct", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]
```

```{r}
# for split by sex
# with alcohol
Reg_Input_Data_Placenta_male_PREDO_EAAR_wa <- Data_PREDO_Placenta_male[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Alcohol_Use_In_Early_Pregnancy_19Oct", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]

# without alcohol
Reg_Input_Data_Placenta_male_PREDO_EAAR_n <- Data_PREDO_Placenta_male[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]


# with alcohol
Reg_Input_Data_Placenta_female_PREDO_EAAR_wa <- Data_PREDO_Placenta_female[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Alcohol_Use_In_Early_Pregnancy_19Oct", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]

# without alcohol
Reg_Input_Data_Placenta_female_PREDO_EAAR_n <- Data_PREDO_Placenta_female[, c("EAAR_Lee", "Child_Sex", "Maternal_Age_18PopRegandBR", "smoking_dichotom", "Delivery_Mode_dichotom", "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight","Birth_Length", "Head_Circumference_at_Birth","Parity_dichotom",  "inducedlabour", "maternal_diabetes_dichotom", "maternal_hypertension_dichotom", "Maternal_Mental_Disorders_By_Childbirth")]

```

data frame without missings
```{r}
Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n <- na.omit(Reg_Input_Data_Placenta_PREDO_EAAR_n) 
dim(Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n)

Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa <- na.omit(Reg_Input_Data_Placenta_PREDO_EAAR_wa) 
dim(Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa)


Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_n <- na.omit(Reg_Input_Data_Placenta_male_PREDO_EAAR_n) 
dim(Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_n)

Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_wa <- na.omit(Reg_Input_Data_Placenta_male_PREDO_EAAR_wa) 
dim(Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_wa)

Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_n <- na.omit(Reg_Input_Data_Placenta_female_PREDO_EAAR_n) 
dim(Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_n)

Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_wa <- na.omit(Reg_Input_Data_Placenta_female_PREDO_EAAR_wa) 
dim(Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_wa)
```


```{r}
skimr::skim(Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n)
```


```{r}
save(Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa.Rdata")
save(Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n.Rdata")
```

```{r}
save(Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_wa, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_wa.Rdata")
save(Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_n, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_n.Rdata")

save(Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_wa, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_wa.Rdata")
save(Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_n, file="InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_n.Rdata")
```


[to the top](#top)  

***
# Sample visualization {#Samples}  
Fig. 1
```{r}
Venn_ITU <- euler(c("CVS"=264, "Placenta \n(fetal side)"=486, "Cord blood"=426, "CVS&Placenta \n(fetal side)"=86, "Placenta \n(fetal side)&Cord blood"=390, "CVS&Cord blood"=73, "CVS&Placenta \n(fetal side)&Cord blood"=66))

Venn_PREDO <- euler(c("Placenta \n(decidual \nside)"=139, "Cord \nblood \n(EPIC)"=149, "Cord blood (450K)"=795, "Placenta \n(decidual \nside)&Cord \nblood \n(EPIC)"=117))

plot(Venn_ITU, counts=TRUE, font=1, cex=2, alpha=0.5, fill=c("grey", "lightgrey", "darkgrey"), labels=F)
grid::grid.text("CVS \nn = 264", x=0.3, y=0.3, gp=gpar(col="black", fontsize=11, font="Arial")) #CVS
grid::grid.text("Placenta \n(fetal side)\nn = 486", x=0.6, y=0.2, gp=gpar(col="black", fontsize=11, font="Arial")) #placenta
grid::grid.text("Cord blood\nn = 426", x=0.5, y=0.8, gp=gpar(col="black", fontsize=11, font="Arial")) #cord
grid::grid.text("73", x=0.35, y=0.55, gp=gpar(col="black", fontsize=10, font="Arial")) #cvs cord
grid::grid.text("86", x=0.43, y=0.26, gp=gpar(col="black", fontsize=10, font="Arial")) #cvs placenta
grid::grid.text("390", x=0.6, y=0.5, gp=gpar(col="black", fontsize=10, font="Arial")) #cord placenta
grid::grid.text("66", x=0.43, y=0.45, gp=gpar(col="black", fontsize=10, font="Arial")) #all

plot(Venn_PREDO, counts=TRUE, font=1, cex=1, alpha=0.5, fill=c("grey", "lightgrey", "darkgrey"), labels=F)
grid::grid.text("Placenta\n(decidual side) \nn = 139", x=0.08, y=0.3, gp=gpar(col="black", fontsize=11, font="Arial")) # placenta
grid::grid.text("Cord blood\n(EPIC) \nn = 149", x=0.37, y=0.3, gp=gpar(col="black", fontsize=11, font="Arial")) # cord epic
grid::grid.text("Cord blood\n(450K) \nn = 795", x=0.72, y=0.5, gp=gpar(col="black", fontsize=11, font="Arial")) # cord 450k
grid::grid.text("117", x=0.23, y=0.3, gp=gpar(col="black", fontsize=10, font="Arial")) # overlap
```
```{r}
ifelse(!dir.exists(file.path(getwd(), "Results/")), dir.create(file.path(getwd(), "Results/")), FALSE)
```

```{r}
ifelse(!dir.exists(file.path(getwd(), "Results/Figures/")), dir.create(file.path(getwd(), "Results/Figures/")), FALSE)
```

```{r, warning=F}
png(filename="Results/Figures/ITU_sample.png", width=2300, height=1500, res=300)
plot(Venn_ITU, counts=TRUE, font=1, cex=2, alpha=0.5, fill=c("grey", "lightgrey", "darkgrey"), labels=F)
grid::grid.text("CVS \nn = 264", x=0.3, y=0.3, gp=gpar(col="black", fontsize=11, font="Arial")) #CVS
grid::grid.text("Placenta \n(fetal side)\nn = 486", x=0.6, y=0.2, gp=gpar(col="black", fontsize=11, font="Arial")) #placenta
grid::grid.text("Cord blood\nn = 426", x=0.5, y=0.8, gp=gpar(col="black", fontsize=11, font="Arial")) #cord
grid::grid.text("73", x=0.35, y=0.55, gp=gpar(col="black", fontsize=10, font="Arial")) #cvs cord
grid::grid.text("86", x=0.43, y=0.26, gp=gpar(col="black", fontsize=10, font="Arial")) #cvs placenta
grid::grid.text("390", x=0.6, y=0.5, gp=gpar(col="black", fontsize=10, font="Arial")) #cord placenta
grid::grid.text("66", x=0.43, y=0.45, gp=gpar(col="black", fontsize=10, font="Arial")) #all
dev.off()
```

```{r, warning=F}
png(filename="Results/Figures/PREDO_sample.png", width=2300, height=1500, res=300)
plot(Venn_PREDO, counts=TRUE, font=1, cex=1, alpha=0.5, fill=c("grey", "lightgrey", "darkgrey"), labels=F)
grid::grid.text("Placenta\n(decidual side) \nn = 139", x=0.08, y=0.3, gp=gpar(col="black", fontsize=11, font="Arial")) # placenta
grid::grid.text("Cord blood\n(EPIC) \nn = 149", x=0.37, y=0.3, gp=gpar(col="black", fontsize=11, font="Arial")) # cord epic
grid::grid.text("Cord blood\n(450K) \nn = 795", x=0.72, y=0.5, gp=gpar(col="black", fontsize=11, font="Arial")) # cord 450k
grid::grid.text("117", x=0.23, y=0.3, gp=gpar(col="black", fontsize=10, font="Arial")) # overlap
dev.off()
```


# ITU Descriptives {#ITUDescriptive}  

*Table 1 & 2*

```{r}
ifelse(!dir.exists(file.path(getwd(), "Results/Figures/diffTissues")), dir.create(file.path(getwd(), "Results/Figures/diffTissues")), FALSE)
```

## ITU CVS
Clock
```{r}
knitr::kable(
  psych::describe(Data_CVS_ITU[ ,c("Gestational_Age_Weeks", "gestage_at_CVS_weeks","DNAmGA_Lee","delta_Lee","zdelta_Lee", "EAAR_Lee", "DNAmGA_Mayne","delta_Mayne","zdelta_Mayne","EAAR_Mayne")])
)
```

Cell types
```{r}
knitr::kable(
  psych::describe(Data_CVS_ITU[ ,c("Trophoblasts", "Stromal", "Hofbauer", "Endothelial", "nRBC", "Syncytiotrophoblast")])
)

Data_cells_cvs_itu <- Data_CVS_ITU[ ,c("Trophoblasts", "Stromal", "Hofbauer", "Endothelial", "nRBC", "Syncytiotrophoblast")]

cells_cvs <- data.frame(psych::describe(Data_CVS_ITU[ ,c("Trophoblasts", "Stromal", "Hofbauer", "Endothelial", "nRBC", "Syncytiotrophoblast")]))
cells_cvs_ <- cells_cvs[ ,c("mean", "sd")]

plot_cells_cvs <- ggplot(cells_cvs, aes(x=as.factor(rownames(cells_cvs)), y=mean)) +
  geom_bar(position=position_dodge(), stat="identity", colour='black') +
  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,position=position_dodge(.9))+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(x ="\nCVS (ITU)")

png(filename="Results/Figures/diffTissues/cvs_cells_itu.png", width=2300, height=1500, res=400)
plot_cells_cvs
dev.off()
```

predictors descriptive
```{r}
CVS_Preds_ITU <- Data_CVS_ITU[,c("Child_Sex", "Delivery_mode_dichotom", "Induced_Labour", "Parity_dichotom", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders", "smoking_dichotom", "maternal_alcohol_use", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth")]
colnames(CVS_Preds_ITU) <- c("child_sex", "delivery_mode", "induced_labor", "parity", "hypertension", "diabetes", "mental_disorders", "smoking", "alcohol", "maternal_age", "maternal_BMI", "birth_weight", "birth_length", "head_circumference")
CVS_Preds_ITU$group <- "ITU"
```

```{r}
CVS_Preds_ITU %>%  
select_if(is.factor) %>% 
Hmisc::describe()
```

```{r}
CVS_Preds_ITU %>%
select_if(is.numeric) %>% 
psych::describe()
```

- model without alcohol

<!-- ```{r} -->
<!-- load("InputData/ClockCalculationsInput/Reg_Input_Data_CVS_ITU_EAAR_n_noNa.Rdata") -->
<!-- ``` -->

```{r}
Reg_Input_Data_CVS_ITU_EAAR_n_noNa %>%
  select_if(is.factor) %>%
  Hmisc::describe()

Reg_Input_Data_CVS_ITU_EAAR_n_noNa %>%
  select_if(is.numeric) %>%
  Hmisc::describe()
```

- model with alcohol
<!-- with alcohol -->
<!-- ```{r} -->
<!-- load("InputData/ClockCalculationsInput/Reg_Input_Data_CVS_ITU_EAAR_wa_noNa.Rdata") -->
<!-- ``` -->

```{r}
Reg_Input_Data_CVS_ITU_EAAR_wa_noNa %>%
  select_if(is.factor) %>%
  Hmisc::describe()

#alcohol use 14.3%
```

```{r}
Reg_Input_Data_CVS_ITU_EAAR_wa_noNa %>%
  select_if(is.numeric) %>%
  Hmisc::describe()
```


## ITU Cord blood
Clocks
```{r, warning=FALSE}
knitr::kable(
psych::describe(Data_Cord_ITU[ ,c("Gestational_Age_Weeks","DNAmGA_Knight","delta_Knight","zdelta_Knight", "EAAR_Knight", "DNAmGA_Bohlin","delta_Bohlin","zdelta_Bohlin", "EAAR_Bohlin")])
)
```

cell types
```{r}
knitr::kable(
  psych::describe(Data_Cord_ITU[ ,c("CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", "nRBC")])
)

Data_cells_cord <- Data_Cord_ITU[ ,c("Sample_Name", "CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", "nRBC")]

cells_cord <- data.frame(psych::describe(Data_Cord_ITU[ ,c("CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", "nRBC")]))
cells_cord <- cells_cord[ ,c("mean", "sd")]
rownames(cells_cord) <- c("CD8T", "CD46", "NK", "Bcell", "Monocytes", "Granulocytes", "nRBC")

plot_cells_cord <- ggplot(cells_cord, aes(x=as.factor(rownames(cells_cord)), y=mean)) +
  geom_bar(position=position_dodge(), stat="identity", colour='black') +
  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,position=position_dodge(.9))+
  labs(x ="\nCord blood (ITU)")+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

png(filename="Results/Figures/diffTissues/cord_cells_itu.png", width=2300, height=1500, res=400)
plot_cells_cord
dev.off()

```

predictors descriptive
```{r}
Cordblood_Preds_ITU <- Data_Cord_ITU[,c("Child_Sex", "Delivery_mode_dichotom", "Induced_Labour", "Parity_dichotom", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders", "smoking_dichotom", "maternal_alcohol_use", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth")]
colnames(Cordblood_Preds_ITU) <- c("child_sex", "delivery_mode", "induced_labor", "parity", "hypertension", "diabetes", "mental_disorders", "smoking", "alcohol", "maternal_age", "maternal_BMI", "birth_weight", "birth_length", "head_circumference")
Cordblood_Preds_ITU$group <- "ITU"
```

```{r}
Cordblood_Preds_ITU %>%  
select_if(is.factor) %>% 
Hmisc::describe()
```

```{r}
Cordblood_Preds_ITU %>%  
select_if(is.numeric) %>% 
Hmisc::describe()
```

- model without alcohol

<!-- ```{r} -->
<!-- load("InputData/ClockCalculationsInput/Reg_Input_Data_Cord_ITU_EAAR_noNa_n.Rdata") -->
<!-- ``` -->

```{r}
Reg_Input_Data_Cord_ITU_EAAR_noNa_n %>%
  select_if(is.factor) %>%
  Hmisc::describe()

Reg_Input_Data_Cord_ITU_EAAR_noNa_n %>%
  select_if(is.numeric) %>%
  Hmisc::describe()
```

- model with alcohol

<!-- ```{r} -->
<!-- load("InputData/ClockCalculationsInput/Reg_Input_Data_Cord_ITU_EAAR_noNa_wa.Rdata") -->
<!-- ``` -->

```{r}
Reg_Input_Data_Cord_ITU_EAAR_noNa_wa %>%
  select_if(is.factor) %>%
  Hmisc::describe()

Reg_Input_Data_Cord_ITU_EAAR_noNa_wa %>%
  select_if(is.numeric) %>%
  Hmisc::describe()

#10.4% maternal alcohol use
```

## ITU Placenta
Clocks
```{r, warning=FALSE}
knitr::kable(
psych::describe(Data_Placenta_ITU[ ,c("Gestational_Age_Weeks","DNAmGA_Lee","delta_Lee","zdelta_Lee", "EAAR_Lee", "DNAmGA_Mayne","delta_Mayne","zdelta_Mayne","EAAR_Mayne", "TimeDifferencePlacenta_birth_sampling")])
)
```

cell types
```{r}
knitr::kable(
  psych::describe(Data_Placenta_ITU[ ,c("Trophoblasts", "Stromal", "Hofbauer", "Endothelial", "nRBC", "Syncytiotrophoblast")])
)

Data_cells_placenta_itu <- Data_Placenta_ITU[ ,c("Trophoblasts", "Stromal", "Hofbauer", "Endothelial", "nRBC", "Syncytiotrophoblast")]

cells_placenta <- data.frame(psych::describe(Data_Placenta_ITU[ ,c("Trophoblasts", "Stromal", "Hofbauer", "Endothelial", "nRBC", "Syncytiotrophoblast")]))
cells_placenta <- cells_placenta[ ,c("mean", "sd")]

plot_cells_placenta <- ggplot(cells_placenta, aes(x=as.factor(rownames(cells_placenta)), y=mean)) +
  geom_bar(position=position_dodge(), stat="identity", colour='black') +
  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,position=position_dodge(.9))+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(x ="\nfetal Placenta (ITU)")

png(filename="Results/Figures/diffTissues/placenta_cells_itu.png", width=2300, height=1500, res=400)
plot_cells_placenta
dev.off()
plot_cells_placenta
```

predictors descriptive
```{r}
Placenta_Preds_ITU <- Data_Placenta_ITU[,c("Child_Sex", "Delivery_mode_dichotom", "Induced_Labour", "Parity_dichotom", "Maternal_Hypertension_dichotom", "Maternal_Diabetes_dichotom", "Maternal_Mental_Disorders", "smoking_dichotom", "maternal_alcohol_use", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth")]
colnames(Placenta_Preds_ITU) <- c("child_sex", "delivery_mode", "induced_labor", "parity", "hypertension", "diabetes", "mental_disorders", "smoking", "alcohol", "maternal_age", "maternal_BMI", "birth_weight", "birth_length", "head_circumference")
Placenta_Preds_ITU$group <- "ITU"
```

```{r}
Placenta_Preds_ITU %>%  
select_if(is.factor) %>% 
Hmisc::describe()
```

```{r}
Placenta_Preds_ITU %>%  
select_if(is.numeric) %>% 
Hmisc::describe()
```

- model without alcohol

<!-- ```{r} -->
<!-- load("InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_ITU_EAAR_noNa_n.Rdata") -->
<!-- ``` -->

```{r}
Reg_Input_Data_Placenta_ITU_EAAR_noNa_n %>%
  select_if(is.factor) %>%
  Hmisc::describe()

Reg_Input_Data_Placenta_ITU_EAAR_noNa_n %>%
  select_if(is.numeric) %>%
  Hmisc::describe()
```

- model with alcohol

<!-- ```{r} -->
<!-- load("InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa.Rdata") -->
<!-- ``` -->

```{r}
Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa %>%
  select_if(is.factor) %>%
  Hmisc::describe()

Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa %>%
  select_if(is.numeric) %>%
  Hmisc::describe()

# alcohol use 10.2%
```


[to the top](#top) 


# PREDO Descriptives {#PREDODescriptive}  

## Cord blood EPIC
Clocks
```{r, warning=FALSE}
knitr::kable(
  psych::describe(Data_PREDO_EPICcord[,c("Gestational_Age","DNAmGA_Knight","delta_Knight","zdelta_Knight", "EAAR_Knight","DNAmGA_Bohlin","delta_Bohlin","zdelta_Bohlin",  "EAAR_Bohlin")])
)
```

cell types
```{r}
knitr::kable(
  psych::describe(Data_PREDO_EPICcord[ ,c("CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", "nRBC")])
)

Data_cells_cord_epic <- Data_PREDO_EPICcord[ ,c("Sample_Name", "CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", "nRBC")]
  

cells_cord_epic <- data.frame(psych::describe(Data_PREDO_EPICcord[ ,c("CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", "nRBC")]))
cells_cord_epic <- cells_cord_epic[ ,c("mean", "sd")]
rownames(cells_cord_epic) <- c("CD8T", "CD4T", "NK", "Bcell", "Monocytes", "Granulocytes", "nRBC")
  

plot_cells_cord_epic <- ggplot(cells_cord_epic, aes(x=as.factor(rownames(cells_cord_epic)), y=mean)) +
  geom_bar(position=position_dodge(), stat="identity", colour='black') +
  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,position=position_dodge(.9))+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(x ="\nCord blood EPIC (PREDO)")

png(filename="Results/Figures/diffTissues/cordepic_cells_predo.png", width=2300, height=1500, res=400)
plot_cells_cord_epic
dev.off()
plot_cells_cord_epic
```

predictors descriptive
```{r}
Cordblood_Preds_PREDO <- Data_PREDO_EPICcord[,c("Child_Sex","Delivery_Mode_dichotom","inducedlabour","Parity_dichotom", "maternal_hypertension_dichotom", "maternal_diabetes_dichotom", "Maternal_Mental_Disorders_By_Childbirth","smoking_dichotom","Alcohol_Use_In_Early_Pregnancy_19Oct","Maternal_Age_18PopRegandBR",   "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth")]
colnames(Cordblood_Preds_PREDO) <- c("child_sex", "delivery_mode", "induced_labor", "parity", "hypertension", "diabetes", "mental_disorders", "smoking", "alcohol", "maternal_age", "maternal_BMI", "birth_weight", "birth_length", "head_circumference")
Cordblood_Preds_PREDO$group <- "PREDO"
levels(Cordblood_Preds_PREDO$induced_labor)[levels(Cordblood_Preds_PREDO$induced_labor)=="Yes"] <- "yes"
levels(Cordblood_Preds_PREDO$induced_labor)[levels(Cordblood_Preds_PREDO$induced_labor)=="No"] <- "no"
levels(Cordblood_Preds_PREDO$diabetes)[levels(Cordblood_Preds_PREDO$diabetes)=="no diabetes in current pregnancy"] <- "no diabetes this pregnancy"
```

```{r}
Cordblood_Preds_PREDO %>%  
select_if(is.factor) %>% 
Hmisc::describe()
```

```{r}
Cordblood_Preds_PREDO %>%
select_if(is.numeric) %>% 
psych::describe()
```


## Cord blood 450K
Clocks
```{r, warning=FALSE}
knitr::kable(
  psych::describe(Data_PREDO_450Kcord[ ,c("Gestational_Age","DNAmGA_Knight","delta_Knight","zdelta_Knight", "EAAR_Knight", "DNAmGA_Bohlin","delta_Bohlin","zdelta_Bohlin", "EAAR_Bohlin")])
)
```

cell types
```{r}
knitr::kable(
  psych::describe(Data_PREDO_450Kcord[ ,c("CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", "nRBC")])
)

Data_cells_cord_450 <- Data_PREDO_450Kcord[ ,c("Sample_Name", "CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", "nRBC")]
  
cells_cord_450K <- data.frame(psych::describe(Data_PREDO_450Kcord[ ,c("CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", "nRBC")]))
cells_cord_450K <- cells_cord_450K[ ,c("mean", "sd")]
rownames(cells_cord_450K) <- c("CD8T", "CD4T", "NK", "Bcell", "Monocytes", "Granulocytes", "nRBC")
  
plot_cells_cord_450K <- ggplot(cells_cord_450K, aes(x=as.factor(rownames(cells_cord_450K)), y=mean)) +
  geom_bar(position=position_dodge(), stat="identity", colour='black') +
  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,position=position_dodge(.9))+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(x ="\nCord blood 450K (PREDO)")

png(filename="Results/Figures/diffTissues/cord450k_cells_predo.png", width=2300, height=1500, res=400)
plot_cells_cord_450K
dev.off()
plot_cells_cord_450K
```

predictors descriptive
```{r}
Cordblood_Preds450K_PREDO <- Data_PREDO_450Kcord[,c("Child_Sex","Delivery_Mode_dichotom","inducedlabour","Parity_dichotom", "maternal_hypertension_dichotom", "maternal_diabetes_dichotom", "Maternal_Mental_Disorders_By_Childbirth","smoking_dichotom","Alcohol_Use_In_Early_Pregnancy_19Oct","Maternal_Age_18PopRegandBR",   "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth")]
colnames(Cordblood_Preds450K_PREDO) <- c("child_sex", "delivery_mode", "induced_labor", "parity", "hypertension", "diabetes", "mental_disorders", "smoking", "alcohol", "maternal_age", "maternal_BMI", "birth_weight", "birth_length", "head_circumference")
Cordblood_Preds450K_PREDO$group <- "PREDO"
levels(Cordblood_Preds450K_PREDO$induced_labor)[levels(Cordblood_Preds450K_PREDO$induced_labor)=="Yes"] <- "yes"
levels(Cordblood_Preds450K_PREDO$induced_labor)[levels(Cordblood_Preds450K_PREDO$induced_labor)=="No"] <- "no"
levels(Cordblood_Preds450K_PREDO$diabetes)[levels(Cordblood_Preds450K_PREDO$diabetes)=="no diabetes in current pregnancy"] <- "no diabetes this pregnancy"
```

```{r}
Cordblood_Preds450K_PREDO %>%  
select_if(is.factor) %>% 
Hmisc::describe()
```

```{r}
Cordblood_Preds450K_PREDO %>%
select_if(is.numeric) %>% 
psych::describe()
```

## Placenta EPIC
Clocks
```{r, warning=FALSE}
knitr::kable(
  psych::describe(Data_PREDO_EPICplacenta[,c("Gestational_Age","DNAmGA_Lee","delta_Lee","zdelta_Lee", "EAAR_Lee", "DNAmGA_Mayne","delta_Mayne","zdelta_Mayne", "EAAR_Mayne")])
)
```

cell types
```{r}
knitr::kable(
  psych::describe(Data_PREDO_EPICplacenta[ ,c("Trophoblasts", "Stromal", "Hofbauer", "Endothelial", "nRBC", "Syncytiotrophoblast")])
)

Data_cells_placenta_pred <- Data_PREDO_EPICplacenta[ ,c("Trophoblasts", "Stromal", "Hofbauer", "Endothelial", "nRBC", "Syncytiotrophoblast")]

cells_placenta_predo <- data.frame(psych::describe(Data_PREDO_EPICplacenta[ ,c("Trophoblasts", "Stromal", "Hofbauer", "Endothelial", "nRBC", "Syncytiotrophoblast")]))
cells_cvs <- cells_cvs[ ,c("mean", "sd")]

plot_cells_placenta_predo <- ggplot(cells_placenta_predo, aes(x=as.factor(rownames(cells_placenta_predo)), y=mean)) +
  geom_bar(position=position_dodge(), stat="identity", colour='black') +
  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,position=position_dodge(.9))+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  labs(x ="\ndecidual Placenta (PREDO)")

png(filename="Results/Figures/diffTissues/placenta_cells_predo.png", width=2300, height=1500, res=400)
plot_cells_placenta_predo
dev.off()
plot_cells_placenta_predo
```

predictors descriptive
```{r}
Placenta_Preds_PREDO <- Data_PREDO_EPICplacenta[,c("Child_Sex","Delivery_Mode_dichotom","inducedlabour","Parity_dichotom", "maternal_hypertension_dichotom", "maternal_diabetes_dichotom", "Maternal_Mental_Disorders_By_Childbirth","smoking_dichotom","Alcohol_Use_In_Early_Pregnancy_19Oct","Maternal_Age_18PopRegandBR",   "Maternal_PrepregnancyBMI18oct28new", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth")]
colnames(Placenta_Preds_PREDO) <- c("child_sex", "delivery_mode", "induced_labor", "parity", "hypertension", "diabetes", "mental_disorders", "smoking", "alcohol", "maternal_age", "maternal_BMI", "birth_weight", "birth_length", "head_circumference")
Placenta_Preds_PREDO$group <- "PREDO"
levels(Placenta_Preds_PREDO$induced_labor)[levels(Placenta_Preds_PREDO$induced_labor)=="Yes"] <- "yes"
levels(Placenta_Preds_PREDO$induced_labor)[levels(Placenta_Preds_PREDO$induced_labor)=="No"] <- "no"
levels(Placenta_Preds_PREDO$diabetes)[levels(Placenta_Preds_PREDO$diabetes)=="no diabetes in current pregnancy"] <- "no diabetes this pregnancy"
```

```{r}
Placenta_Preds_PREDO %>%  
select_if(is.factor) %>% 
Hmisc::describe()
```

```{r}
Placenta_Preds_PREDO %>%
select_if(is.numeric) %>% 
psych::describe()
```

- model without alcohol

<!-- ```{r} -->
<!-- load("InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n.Rdata") -->
<!-- ``` -->

```{r}
Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n %>%
  select_if(is.factor) %>%
  Hmisc::describe()

Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n %>%
  select_if(is.numeric) %>%
  Hmisc::describe()
```

- model with alcohol

<!-- ```{r} -->
<!-- load("InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa.Rdata") -->
<!-- ``` -->

```{r}
Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa %>%
  select_if(is.factor) %>%
  Hmisc::describe()

Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa %>%
  select_if(is.numeric) %>%
  Hmisc::describe()

#12.3% maternal alcohol use
```

[to the top](#top) 

# Cell Type Overview
**Cell Type Overview ITU & PREDO**
```{r}
#grid.arrange(plot_cells_cord, plot_cells_cord_epic, plot_cells_cord_450K, ncol=3)

ggarrange(plot_cells_cord +
               theme(axis.ticks.y = element_blank(),
                     plot.margin = margin(r = 1) ), 
          plot_cells_cord_epic + 
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(),
                     axis.title.y = element_blank(),
                     plot.margin = margin(r = 1, l = 1) ), 
          plot_cells_cord_450K + 
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(),
                     axis.title.y = element_blank(),
                     plot.margin = margin(l = 1)  ),
          nrow = 1)

ggarrange(plot_cells_cvs +
               theme(axis.ticks.y = element_blank(),
                     plot.margin = margin(r = 1) ), 
          plot_cells_placenta + 
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(),
                     axis.title.y = element_blank(),
                     plot.margin = margin(r = 1, l = 1) ), 
          plot_cells_placenta_predo + 
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(),
                     axis.title.y = element_blank(),
                     plot.margin = margin(l = 1)  ),
          nrow = 1)
```

[to the top](#top) 

# comparison PREDO & ITU in predictors {#predictorsITUPREDO}  
## placenta
```{r}
Placenta_Preds <- rbind(Placenta_Preds_ITU, Placenta_Preds_PREDO)
```

continuous predictors, t-test
```{r}
placenta_pred_t <- Placenta_Preds %>% 
  select_if(is.numeric) %>%
  map_df(~ broom::tidy(t.test(. ~ Placenta_Preds$group)), .id = 'var')

placenta_pred_t 
```

```{r}
t.test(maternal_age ~ group, data=Placenta_Preds)$estimate
t.test(maternal_BMI ~ group, data=Placenta_Preds)$estimate
t.test(birth_weight ~ group, data=Placenta_Preds)$estimate
t.test(birth_length ~ group, data=Placenta_Preds)$estimate
```

```{r}
p.adjust(placenta_pred_t$p.value, method = "bonferroni", n = 15)
```

categorical
```{r}
placenta_pred_chi <- Placenta_Preds %>% 
  select_if(is.factor) %>%
  map_df(~ broom::tidy(chisq.test(. ,Placenta_Preds$group, correct=F)), .id = 'var')

placenta_pred_chi
```

```{r}
p.adjust(placenta_pred_chi$p.value, method = "bonferroni", n = 15)
```

```{r}
table(Placenta_Preds$delivery_mode, Placenta_Preds$group)
table(Placenta_Preds$hypertension, Placenta_Preds$group)
table(Placenta_Preds$diabetes, Placenta_Preds$group)
table(Placenta_Preds$smoking, Placenta_Preds$group)
```

## cordblood EPIC

```{r}
Cordblood_Preds <- rbind(Cordblood_Preds_ITU, Cordblood_Preds_PREDO)
```

continuous predictors, t-test
```{r}
cordblood_pred_t <- Cordblood_Preds %>% 
  select_if(is.numeric) %>%
  map_df(~ broom::tidy(t.test(. ~ Cordblood_Preds$group)), .id = 'var')

cordblood_pred_t 
# maternal age, maternal BMI
```
```{r}
t.test(maternal_age ~ group, data=Cordblood_Preds)$estimate
t.test(maternal_BMI ~ group, data=Cordblood_Preds)$estimate
```

```{r}
p.adjust(cordblood_pred_t$p.value, method = "bonferroni", n = 15)
# only maternal age
```

categorical
```{r}
cordblood_pred_chi <- Cordblood_Preds %>% 
  select_if(is.factor) %>%
  map_df(~ broom::tidy(chisq.test(. ,Cordblood_Preds$group, correct=F)), .id = 'var')

cordblood_pred_chi
# parity, hypertension, smoking
```

```{r}
p.adjust(cordblood_pred_chi$p.value, method = "bonferroni", n = 15)
# only hypertension
```

```{r}
table(Cordblood_Preds$delivery_mode, Cordblood_Preds$group)
table(Cordblood_Preds$hypertension, Cordblood_Preds$group)
table(Cordblood_Preds$diabetes, Cordblood_Preds$group)
table(Cordblood_Preds$smoking, Cordblood_Preds$group)
```

## cordblood 450K
```{r}
Cordblood_Preds450K <- rbind(Cordblood_Preds_ITU, Cordblood_Preds450K_PREDO)
```

continuous predictors, t-test
```{r}
cordblood_pred450K_t <- Cordblood_Preds450K %>% 
  select_if(is.numeric) %>%
  map_df(~ broom::tidy(t.test(. ~ Cordblood_Preds450K$group)), .id = 'var')

cordblood_pred450K_t 
# maternal age and BMI
```

```{r}
t.test(maternal_age ~ group, data=Cordblood_Preds450K)$estimate
t.test(maternal_BMI ~ group, data=Cordblood_Preds450K)$estimate
```

```{r}
p.adjust(cordblood_pred450K_t$p.value, method = "bonferroni", n = 15)
```

categorical
```{r}
cordblood_pred450K_chi <- Cordblood_Preds450K %>% 
  select_if(is.factor) %>%
  map_df(~ broom::tidy(chisq.test(. ,Cordblood_Preds450K$group, correct=F)), .id = 'var')

cordblood_pred450K_chi
# parity, hypertension, diabetes, alcohol
```

```{r}
p.adjust(cordblood_pred450K_chi$p.value, method = "bonferroni", n = 15)
# only parity, hypertension
```

```{r}
table(Cordblood_Preds450K$parity, Cordblood_Preds450K$group)
table(Cordblood_Preds450K$hypertension, Cordblood_Preds450K$group)
table(Cordblood_Preds450K$diabetes, Cordblood_Preds450K$group)
table(Cordblood_Preds450K$alcohol, Cordblood_Preds450K$group)
```
[to the top](#top)

# Predictors correlations
Fig. 2   

## ITU: look at predictors, in full data (all persons) {#PredictorsITUAll}  

```{r}
ifelse(!dir.exists(file.path(getwd(), "Results/Figures/predictors_cors")), dir.create(file.path(getwd(), "Results/Figures/predictors_cors")), FALSE)
```

```{r}
Input_ITU_all <- Data_ITU_all[ ,!(names(Data_ITU_all) %in% c("Sample_Name", "PC1_ethnicity", "PC2_ethnicity"))]
names(Input_ITU_all) <- c("child sex", "maternal age", "maternal smooking", "delivery mode", "maternal BMI", "birth weight", "birth length", "head circumference", "Parity", "induced labor", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal alcohol use")
```

```{r}
Input_M_all <- model.matrix(~0+., data=Input_ITU_all)
colnames(Input_M_all) <- c("male","female", "maternal age", "maternal smoking", "delivery mode", "maternal BMI", "birth weight", "birth length", "head circumference", "parity", "induced labor", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal alcohol use")
```

```{r}
Input_M_all %>%
  cor(use="pairwise.complete.obs") %>% 
  corrplot(type="upper", tl.col="black")
```

```{r}
png("Results/Figures/predictors_cors/ITU_all.png", width=1600, height= 1500, res=350)
Input_M_all %>%
  cor(use="pairwise.complete.obs") %>% 
  corrplot(type="upper", tl.col="black")
  theme(plot.margin=unit(c(-0.30,0,0,0), "null")) # remove margin around plot
dev.off()
```


```{r}
corr.test(Input_ITU_all[6:8])
```

[to the top](#top)

## PREDO: look at predictors, in full data (all persons) {#PredictorsPREDOAll}

```{r}
Input_PREDO_EPIC_all <- Data_PREDO_EPIC_all[ ,!(names(Data_PREDO_EPIC_all) %in% c("Sample_Name", "PC1", "PC2"))]
names(Input_PREDO_EPIC_all) <- c("child sex", "maternal age", "maternal smooking", "delivery mode", "maternal BMI", "birth weight", "birth length", "head circumference", "parity", "induced labor", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal alcohol use")
```

```{r}
Input_M_PREDO_EPIC_all <- model.matrix(~0+., data=Input_PREDO_EPIC_all)
colnames(Input_M_PREDO_EPIC_all) <- c("male","female", "maternal age", "maternal smoking", "delivery mode", "maternal BMI", "birth weight", "birth length", "head circumference", "parity", "induced labor", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal alcohol use")
```

```{r}
Input_M_PREDO_EPIC_all %>%
  cor(use="pairwise.complete.obs") %>% 
  corrplot(type="upper", tl.col="black")
```

```{r}
png("Results/Figures/predictors_cors/PREDO_EPIC_all.png", width=1600, height= 1500, res=350)
Input_M_PREDO_EPIC_all %>%
  cor(use="pairwise.complete.obs") %>% 
  corrplot(type="upper", tl.col="black")
dev.off()
# mar = c(0, 0, 0, 2)
```


```{r}
corr.test(Input_PREDO_EPIC_all[6:8])
```

# correlation DNAmGA-GA
Additional file 7, Table 2

## ITU: gestational age epigenetic age correlation (separate for every tissue) {#corDNAmGAGAITU}

```{r}
ifelse(!dir.exists(file.path(getwd(), "Results/Figures/corDNAmGAGA")), dir.create(file.path(getwd(), "Results/Figures/corDNAmGAGA")), FALSE)
```

**CVS**  
*Lee clock*
```{r}
cor.test(Data_CVS_ITU$DNAmGA_Lee, Data_CVS_ITU$gestage_at_CVS_weeks, method="pearson")


corCVSGA_Lee <- ggscatter(Data_CVS_ITU, x = "gestage_at_CVS_weeks", y = "DNAmGA_Lee", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at sampling (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="CVS", subtitle="Lee clock")

plotCVSGA_Lee <- ggplot(Data_CVS_ITU, aes(x =gestage_at_CVS_weeks, y =DNAmGA_Lee))+ 
  geom_point(shape=1)+
  xlab("gestational age at sampling (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("CVS \nLee clock")

grid.arrange(corCVSGA_Lee, plotCVSGA_Lee, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Lee_CVS_ITU.tiff", units="in", width=8, height=5, res=300)
corCVSGA_Lee
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Lee_CVS_ITU.tiff", units="in", width=8, height=5, res=300)
plotCVSGA_Lee
dev.off()
```


*Mayne clock:*
```{r}
cor.test(Data_CVS_ITU$DNAmGA_Mayne, Data_CVS_ITU$gestage_at_CVS_weeks, method="pearson")

corCVSGA_Mayne <- ggscatter(Data_CVS_ITU, x = "gestage_at_CVS_weeks", y = "DNAmGA_Mayne", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at sampling (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title=" CVS", subtitle="Mayne clock")

plotCVSGA_Mayne <- ggplot(Data_CVS_ITU, aes(x =gestage_at_CVS_weeks, y =DNAmGA_Mayne))+ 
  geom_point(shape=1)+
  xlab("gestational age at sampling (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("CVS \nMayne clock")

grid.arrange(corCVSGA_Mayne, plotCVSGA_Mayne, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Mayne_CVS_ITU.tiff", units="in", width=8, height=5, res=300)
corCVSGA_Mayne
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Mayne_CVS_ITU.tiff", units="in", width=8, height=5, res=300)
plotCVSGA_Mayne
dev.off()
```

**Cordblood**  
*Knight clock*
```{r}
cor.test(Data_Cord_ITU$DNAmGA_Knight, Data_Cord_ITU$Gestational_Age_Weeks, method="pearson")

corCordGA_Knight <- ggscatter(Data_Cord_ITU, x = "Gestational_Age_Weeks", y = "DNAmGA_Knight", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at birth (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Cordblood", subtitle="Knight clock")

plotCordGA_Knight <- ggplot(Data_Cord_ITU, aes(x =Gestational_Age_Weeks, y =DNAmGA_Knight))+ 
  geom_point(shape=1)+
  xlab("gestational age at birth (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Cordblood \nKnight clock")

grid.arrange(corCordGA_Knight, plotCordGA_Knight, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Cord_Knight_ITU.tiff", units="in", width=8, height=5, res=300)
corCordGA_Knight
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Cord_Knight_ITU.tiff", units="in", width=8, height=5, res=300)
plotCordGA_Knight
dev.off()

## Knight Testing Data set correlation: r=0.91; individual test sets r=0.52 & 0.65)
## Girchenko correlation r=0.51
## Palma-Gudiel: r=0.76
## Suarez: r=.0.52
```


*Bohlin Clock*
```{r}
cor.test(Data_Cord_ITU$DNAmGA_Bohlin, Data_Cord_ITU$Gestational_Age_Weeks, method="pearson")

corCordGA_Bohlin <- ggscatter(Data_Cord_ITU, x = "Gestational_Age_Weeks", y = "DNAmGA_Bohlin", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at birth (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Cordblood", subtitle="Bohlin clock")

plotCordGA_Bohlin <- ggplot(Data_Cord_ITU, aes(x = Gestational_Age_Weeks, y =DNAmGA_Bohlin))+ 
  geom_point(shape=1)+
  xlab("gestational age at birth (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Cordblood \nBohlin clock")

grid.arrange(corCordGA_Bohlin, plotCordGA_Bohlin, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Cord_Bohlin_ITU.tiff", units="in", width=8, height=5, res=300)
corCordGA_Bohlin
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Cord_Bohlin_ITU.tiff", units="in", width=8, height=5, res=300)
plotCordGA_Bohlin
dev.off()

## Simpkin correlation in ALSPAC r=0.65
```

**Placenta**  
*Lee Clock*
```{r}
cor.test(Data_Placenta_ITU$DNAmGA_Lee, Data_Placenta_ITU$Gestational_Age_Weeks, method="pearson")

corPlacentaGA_Lee <- ggscatter(Data_Placenta_ITU, x = "Gestational_Age_Weeks", y = "DNAmGA_Lee", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at birth (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Placenta", subtitle="Lee clock")

plotPlacentaGA_Lee <- ggplot(Data_Placenta_ITU, aes(x =Gestational_Age_Weeks, y=DNAmGA_Lee))+ 
  geom_point(shape=1)+
  xlab("gestational age at birth (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Placenta \nLee clock")

grid.arrange(corPlacentaGA_Lee, plotPlacentaGA_Lee, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Placenta_Lee_ITU.tiff", units="in", width=8, height=5, res=300)
corPlacentaGA_Lee
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Placenta_Lee_ITU.tiff", units="in", width=8, height=5, res=300)
plotPlacentaGA_Lee
dev.off()
```


*Mayne Clock*
```{r}
cor.test(Data_Placenta_ITU$DNAmGA_Mayne, Data_Placenta_ITU$Gestational_Age_Weeks, method="pearson")

corPlacentaGA_Mayne <- ggscatter(Data_Placenta_ITU, x = "Gestational_Age_Weeks", y = "DNAmGA_Mayne", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at birth (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Placenta", subtitle="Mayne clock")

plotPlacentaGA_Mayne <- ggplot(Data_Placenta_ITU, aes(x =Gestational_Age_Weeks, y =DNAmGA_Mayne))+ 
  geom_point(shape=1)+
  xlab("gestational age at birth (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Placenta \nMayne")

grid.arrange(corPlacentaGA_Mayne, plotPlacentaGA_Mayne, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Placenta_Mayne_ITU.tiff", units="in", width=8, height=5, res=300)
corPlacentaGA_Mayne
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Placenta_Mayne_ITU.tiff", units="in", width=8, height=5, res=300)
plotPlacentaGA_Mayne 
dev.off()
```

[to the top](#top) 

## PREDO: gestational age epigenetic age correlation (separate for every tissue) {#corDNAmGAGAPREDO}  

**450K Cordblood**
*Knight*
with the full estimator, Knight
```{r}
cor.test(Data_PREDO_450Kcord$DNAmGA_Knight, Data_PREDO_450Kcord$Gestational_Age, method="pearson")

corCord_Knight_P450 <- ggscatter(Data_PREDO_450Kcord, x = "Gestational_Age", y = "DNAmGA_Knight", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at sampling (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Cordblood (450K)", subtitle="Knight clock")

plotCord_Knight_P450 <- ggplot(Data_PREDO_450Kcord, aes(x =Gestational_Age, y =DNAmGA_Knight))+ 
  geom_point(shape=1)+
  xlab("gestational age at sampling (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Cordblood (450K) \nKnight clock")

grid.arrange(corCord_Knight_P450, plotCord_Knight_P450, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Cord450K_Knight_PREDO.tiff", units="in", width=8, height=5, res=300)
corCord_Knight_P450
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Cord450K_Knight_PREDO.tiff", units="in", width=8, height=5, res=300)
plotCord_Knight_P450 
dev.off()
```


```{r}
#Data_PREDO_450Kcord[which.min(Data_PREDO_450Kcord$Gestational_Age),] #(visual) outlier, row 70
# exclude this outlier to see what correlation would be then
cor.test(Data_PREDO_450Kcord$DNAmGA_Knight[-70], Data_PREDO_450Kcord$Gestational_Age[-70], method="pearson")

Data_PREDO_450Kcord_outout <- Data_PREDO_450Kcord[-70, ]
ggscatter(Data_PREDO_450Kcord_outout, x = "Gestational_Age", y = "DNAmGA_Knight", 
         add = "reg.line", conf.int = TRUE, 
         cor.coef = TRUE, cor.method = "pearson",
         xlab = "gestational age at sampling (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Cordblood (450K)", subtitle="with outlier removed")
```


*Bohlin*
with the full estimator
```{r}
cor.test(Data_PREDO_450Kcord$DNAmGA_Bohlin, Data_PREDO_450Kcord$Gestational_Age, method="pearson")

corCord_Bohlin_P450 <- ggscatter(Data_PREDO_450Kcord, x = "Gestational_Age", y = "DNAmGA_Bohlin", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at sampling (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Cordblood (450K)", subtitle="Bohlin clock")

plotCord_Bohlin_P450 <- ggplot(Data_PREDO_450Kcord, aes(x =Gestational_Age, y =DNAmGA_Bohlin))+ 
  geom_point(shape=1)+
  xlab("gestational age at sampling (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Cordblood (450K) \nBohlin")

grid.arrange(corCord_Bohlin_P450, plotCord_Bohlin_P450, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Cord450K_Bohlin_PREDO.tiff", units="in", width=10, height=5, res=300)
corCord_Bohlin_P450
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Cord450K_Bohlin_PREDO.tiff", units="in", width=10, height=5, res=300)
plotCord_Bohlin_P450
dev.off()
```

**EPIC Cordblood**  
*Knight*
```{r}
cor.test(Data_PREDO_EPICcord$DNAmGA_Knight, Data_PREDO_EPICcord$Gestational_Age, method="pearson")

corCord_Knight_P <- ggscatter(Data_PREDO_EPICcord, x = "Gestational_Age", y = "DNAmGA_Knight", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at sampling (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Cordblood (EPIC)", subtitle="Knight clock")

plotCord_Knight_P <- ggplot(Data_PREDO_EPICcord, aes(x =Gestational_Age, y =DNAmGA_Knight))+ 
  geom_point(shape=1)+
  xlab("gestational age at sampling (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Cordblood (EPIC) \nKnight clock")

grid.arrange(corCord_Knight_P, plotCord_Knight_P, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Cord_Knight_PREDO.tiff", units="in", width=10, height=5, res=300)
corCord_Knight_P
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Cord_Knight_PREDO.tiff", units="in", width=10, height=5, res=300)
plotCord_Knight_P
dev.off()
```

*Bohlin*:
```{r}
cor.test(Data_PREDO_EPICcord$DNAmGA_Bohlin, Data_PREDO_EPICcord$Gestational_Age, method="pearson")

corCord_Bohlin_P <- ggscatter(Data_PREDO_EPICcord, x = "Gestational_Age", y = "DNAmGA_Bohlin", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at sampling (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Cordblood (EPIC)", subtitle="Bohlin clock")

plotCord_Bohlin_P <- ggplot(Data_PREDO_EPICcord, aes(x =Gestational_Age, y =DNAmGA_Bohlin))+ 
  geom_point(shape=1)+
  xlab("gestational age at sampling (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Cordblood (EPIC) \nBohlin")

grid.arrange(corCord_Bohlin_P, plotCord_Bohlin_P, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Cord_Bohlin_PREDO.tiff", units="in", width=10, height=5, res=300)
corCord_Bohlin_P
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Cord_Bohlin_PREDO.tiff", units="in", width=10, height=5, res=300)
plotCord_Bohlin_P
dev.off()
```

**EPIC Placenta**  
*Lee*
```{r}
cor.test(Data_PREDO_EPICplacenta$DNAmGA_Lee, Data_PREDO_EPICplacenta$Gestational_Age, method="pearson")

corPlacenta_Lee_P <- ggscatter(Data_PREDO_EPICplacenta, x = "Gestational_Age", y = "DNAmGA_Lee", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at sampling (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Placenta (EPIC)", subtitle="Lee clock")

plotPlacenta_Lee_P <- ggplot(Data_PREDO_EPICplacenta, aes(x =Gestational_Age, y =DNAmGA_Lee))+ 
  geom_point(shape=1)+
  xlab("gestational age at sampling (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Placenta (EPIC) \nLee clock")

grid.arrange(corPlacenta_Lee_P, plotPlacenta_Lee_P, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Placenta_Lee_PREDO.tiff", units="in", width=10, height=5, res=300)
corPlacenta_Lee_P
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Placenta_Lee_PREDO.tiff", units="in", width=10, height=5, res=300)
plotPlacenta_Lee_P
dev.off()
```

*Mayne*
```{r}
cor.test(Data_PREDO_EPICplacenta$DNAmGA_Mayne, Data_PREDO_EPICplacenta$Gestational_Age, method="pearson")

corPlacenta_Mayne_P <- ggscatter(Data_PREDO_EPICplacenta, x = "Gestational_Age", y = "DNAmGA_Mayne", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age at sampling (weeks)", ylab = "predicted gestational age from DNAm (weeks)", title="Placenta (EPIC)", subtitle="Mayne clock")

plotPlacenta_Mayne_P <- ggplot(Data_PREDO_EPICplacenta, aes(x =Gestational_Age, y =DNAmGA_Mayne))+ 
  geom_point(shape=1)+
  xlab("gestational age at sampling (weeks)")+
  ylab("predicted gestational age from DNAm (weeks)")+
  geom_abline(intercept = 0, slope = 1)+
  ggtitle("Placenta (EPIC) \nMayne")

grid.arrange(corPlacenta_Mayne_P, plotPlacenta_Mayne_P, ncol=2)

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_cor_Placenta_Mayne_PREDO.tiff", units="in", width=10, height=5, res=300)
corPlacenta_Mayne_P
dev.off()

tiff("Results/Figures/corDNAmGAGA/DNAmGAGA_plot_Placenta_Mayne_PREDO.tiff", units="in", width=10, height=5, res=300)
plotPlacenta_Mayne_P
dev.off()
```


[to the top](#top) 


### DNAmGA GA correlation plots {#PlotcorDNAmGAGA}  
for Additional File 7
```{r}
cor_bohlin_itu <- ggscatter(Data_Cord_ITU, x = "Gestational_Age_Weeks", y = "DNAmGA_Bohlin", 
          add = "reg.line", conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Bohlin (weeks)", subtitle="ITU (n=426)")+
   stat_cor(label.x = 28, label.y=43,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank())+
  scale_y_continuous(limits = c(32,44), breaks = seq(32,44, by=2))+
 scale_x_continuous(limits = c(28,44), breaks = seq(28,44, by=2))


cor_bohlin_predo <- ggscatter(Data_PREDO_EPICcord, x = "Gestational_Age", y = "DNAmGA_Bohlin", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Bohlin Clock (weeks)", subtitle="PREDO 450K (n=149)")+
   stat_cor(label.x = 30, label.y=43,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(32,44), breaks = seq(32,44, by=2))+
  scale_x_continuous(limits = c(30,44), breaks = seq(30,44, by=2))
  
cor_bohlin_predo_450k <- ggscatter(Data_PREDO_450Kcord, x = "Gestational_Age", y = "DNAmGA_Bohlin", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Bohlin Clock (weeks)", subtitle="PREDO EPIC (n=793)")+
   stat_cor(label.x = 26, label.y=43,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(32,44), breaks = seq(32,44, by=2))+
  scale_x_continuous(limits = c(26,44), breaks = seq(26,44, by=2))

Bohlin_DNAmGA_GA <- ggarrange(
          cor_bohlin_itu +
           theme(plot.margin = margin(r = 0.2)),
          cor_bohlin_predo +
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(), axis.title.y = element_blank(), plot.margin = margin(r = 0.2, l = 0.2)),
          cor_bohlin_predo_450k +
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(), axis.title.y = element_blank(), plot.margin = margin(r = 0.2, l = 0.2)),
          nrow = 1,
          align = c("hv"))

# Annotate the figure by adding a common labels
annotate_figure(Bohlin_DNAmGA_GA,
                bottom = text_grob("Gestational Age (weeks)", size = 12))

```

```{r}
png(file="Results/Figures/corDNAmGAGA/Bohlin.png", width= 3600, height=2100, res=480)
annotate_figure(Bohlin_DNAmGA_GA,
                bottom = text_grob("Gestational Age (weeks)", size = 12))
dev.off()
```

```{r}
cor_knight_itu <- ggscatter(Data_Cord_ITU, x = "Gestational_Age_Weeks", y = "DNAmGA_Knight", 
          add = "reg.line", conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Knight Clock (weeks)", subtitle="ITU (n=426)")+
   stat_cor(label.x = 28, label.y=48,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank())+
  scale_y_continuous(limits = c(28,48), breaks = seq(28,48, by=2))+
 scale_x_continuous(limits = c(28,44), breaks = seq(28,44, by=2))


cor_knight_predo <- ggscatter(Data_PREDO_EPICcord, x = "Gestational_Age", y = "DNAmGA_Knight", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Knight Clock (weeks)", subtitle="PREDO EPIC (n=149)")+
   stat_cor(label.x = 30, label.y=48,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(28,48), breaks = seq(28,48, by=2))+
  scale_x_continuous(limits = c(30,44), breaks = seq(30,44, by=2))
  
cor_knight_predo_450k <- ggscatter(Data_PREDO_450Kcord, x = "Gestational_Age", y = "DNAmGA_Knight", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Knight Clock (weeks)", subtitle="PREDO 450K (n=793)")+
   stat_cor(label.x = 26, label.y=48,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(28,48), breaks = seq(28,48, by=2))+
  scale_x_continuous(limits = c(26,44), breaks = seq(26,44, by=2))

Knight_DNAmGA_GA <- ggarrange(
          cor_knight_itu +
           theme(legend.position="none", plot.margin = margin(r = 0.2) ),
          cor_knight_predo +
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(), axis.title.y = element_blank(), plot.margin = margin(r = 0.2, l = 0.2)),
          cor_knight_predo_450k +
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(), axis.title.y = element_blank(), plot.margin = margin(r = 0.2, l = 0.2)),
          nrow = 1,
          align = c("hv"))

# Annotate the figure by adding a common labels
annotate_figure(Knight_DNAmGA_GA,
                bottom = text_grob("Gestational Age (weeks)", size = 12))

```


```{r}
png(file="Results/Figures/corDNAmGAGA/Knight.png", width= 3600, height=2100, res=480)
annotate_figure(Knight_DNAmGA_GA,
                bottom = text_grob("Gestational Age (weeks)", size = 12))
dev.off()
```

```{r}
cor_mayne_itu_cvs <- ggscatter(Data_CVS_ITU, x = "gestage_at_CVS_weeks", y = "DNAmGA_Mayne", 
          add = "reg.line", conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Mayne Clock (weeks)", subtitle="ITU CVS (n=264)")+
   stat_cor(label.x = 10, label.y=20,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_text(size=12),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank())+
  scale_y_continuous(limits = c(4,20), breaks = seq(4,20, by=2))+
 scale_x_continuous(limits = c(10,16), breaks = seq(10,16, by=2))


cor_mayne_itu <- ggscatter(Data_Placenta_ITU, x = "Gestational_Age_Weeks", y = "DNAmGA_Mayne", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Mayne Clock (weeks)", subtitle="ITU (n=486)")+
   stat_cor(label.x = 28, label.y=38,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(25,38), breaks = seq(26,38, by=2))+
  scale_x_continuous(limits = c(28,44), breaks = seq(28,44, by=2))
  
cor_mayne_predo <- ggscatter(Data_PREDO_EPICplacenta, x = "Gestational_Age", y = "DNAmGA_Mayne", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Mayne Clock (weeks)", subtitle="PREDO (n=139)")+
   stat_cor(label.x = 32, label.y=38,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(25,38), breaks = seq(26,38, by=2))+
  scale_x_continuous(limits = c(32,44), breaks = seq(32,44, by=2))

Mayne_DNAmGA_GA <- ggarrange(
          cor_mayne_itu +
           theme(legend.position="none", plot.margin = margin(r = 0.2) ),
          cor_mayne_predo +
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(), axis.title.y = element_blank(), plot.margin = margin(r = 0.2, l = 0.2)),
          nrow = 1,
          align = c("hv"))

# Annotate the figure by adding a common labels
annotate_figure(Mayne_DNAmGA_GA,
                bottom = text_grob("Gestational Age (weeks)", size = 12))

```


```{r}
png(file="Results/Figures/corDNAmGAGA/Mayne.png", width= 2400, height=2100, res=480)
annotate_figure(Mayne_DNAmGA_GA,
                bottom = text_grob("Gestational Age (weeks)", size = 12))
dev.off()

png(file="Results/Figures/corDNAmGAGA/Mayne_CVS.png", width= 800, height=1400, res=320)
cor_mayne_itu_cvs
dev.off()
```

```{r}
cor_lee_itu_cvs <- ggscatter(Data_CVS_ITU, x = "gestage_at_CVS_weeks", y = "DNAmGA_Lee", 
          add = "reg.line", conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Lee Clock (weeks)", subtitle="ITU CVS (n=264)")+
   stat_cor(label.x = 10, label.y=20,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_text(size=12),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank())+
  scale_y_continuous(limits = c(4,20), breaks = seq(4,20, by=2))+
 scale_x_continuous(limits = c(10,16), breaks = seq(10,16, by=2))


cor_lee_itu <- ggscatter(Data_Placenta_ITU, x = "Gestational_Age_Weeks", y = "DNAmGA_Lee", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Lee Clock (weeks)", subtitle="ITU (n=486)")+
   stat_cor(label.x = 28, label.y=44,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(30,44), breaks = seq(30,44, by=2))+
  scale_x_continuous(limits = c(28,44), breaks = seq(28,44, by=2))
  
cor_lee_predo <- ggscatter(Data_PREDO_EPICplacenta, x = "Gestational_Age", y = "DNAmGA_Lee", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "Gestational Age (weeks)", ylab = "DNAmGA Lee Clock (weeks)", subtitle="PREDO (n=139)")+
   stat_cor(label.x = 32, label.y=44,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(30,44), breaks = seq(30,44, by=2))+
  scale_x_continuous(limits = c(32,44), breaks = seq(32,44, by=2))

Lee_DNAmGA_GA <- ggarrange(
          cor_lee_itu +
           theme(legend.position="none", plot.margin = margin(r = 0.2) ),
          cor_lee_predo +
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(), axis.title.y = element_blank(), plot.margin = margin(r = 0.2, l = 0.2)),
          nrow = 1,
          align = c("hv"))

# Annotate the figure by adding a common labels
annotate_figure(Lee_DNAmGA_GA,
                bottom = text_grob("Gestational Age (weeks)", size = 12))

```

```{r}
png(file="Results/Figures/corDNAmGAGA/Lee.png", width= 2400, height=2100, res=480)
annotate_figure(Lee_DNAmGA_GA,
                bottom = text_grob("Gestational Age (weeks)", size = 12))
dev.off()

png(file="Results/Figures/corDNAmGAGA/Lee_CVS.png", width= 800, height=1400, res=320)
cor_lee_itu_cvs
dev.off()
```

# Correlation Clocks
## correlation cordblood clocks {#corCordClocks}  


```{r}
ifelse(!dir.exists(file.path(getwd(), "Results/Figures/corClocks")), dir.create(file.path(getwd(), "Results/Figures/corClocks")), FALSE)
```


```{r}
cor_cord_clocks_itu <- 
  ggscatter(Data_Cord_ITU, x = "DNAmGA_Knight", y = "DNAmGA_Bohlin", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "DNAmGA estimated by the Knight Clock", ylab = "DNAmGA estimated by the Bohlin Clock (weeks)", subtitle="ITU (n=426)")+
   stat_cor(label.x = 30, label.y=43,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(32,44), breaks = seq(32, 44, by=2))+
  scale_x_continuous(limits = c(30,44), breaks = seq(30, 44, by=2))
  #coord_cartesian(ylim = c(32,43))

cor_cord_clocks_predo <-ggscatter(Data_PREDO_EPICcord, x = "DNAmGA_Knight", y = "DNAmGA_Bohlin", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "DNAmGA estimated by the Knight Clock", ylab = "DNAmGA estimated by the Bohlin Clock", subtitle="PREDO EPIC (n=149)")+
   stat_cor(label.x = 30,label.y=43, p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_blank(), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(32,44), breaks = seq(32, 44, by=2))+
  scale_x_continuous(limits = c(30,44), breaks = seq(30, 44, by=2))
 # coord_cartesian(ylim = c(32,43))

cor_cord_clocks_predo_450k <- ggscatter(Data_PREDO_450Kcord, x = "DNAmGA_Knight", y = "DNAmGA_Bohlin",
          add = "reg.line", conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = "pearson",
          xlab = "DNAmGA estimated by the Knight Clock", ylab = "DNAmGA estimated by the Bohlin Clock", subtitle="PREDO 450K (n=795)")+
   stat_cor(label.x = 30, label.y=43,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_blank(), axis.title.x=element_blank(), legend.title = element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(32,44), breaks = seq(32, 44, by=2))+
  scale_x_continuous(breaks = seq(30, 44, by=2))
 # coord_cartesian(ylim = c(32,43))

#ggarrange(grobs=cor_cord_clocks_itu, cor_cord_clocks_predo, cor_cord_clocks_predo_450k, nrow=1, align=c("hv"), top="Correlation Cord blood Clocks")

clock_cord_cor_gg <- ggarrange(
          cor_cord_clocks_itu +
           theme(legend.position="none", plot.margin = margin(r = 0.2) ),
          cor_cord_clocks_predo +
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(), axis.title.y = element_blank(), plot.margin = margin(r = 0.2, l = 0.2)),
          cor_cord_clocks_predo_450k +
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(),
                     plot.margin = margin(l = 0.2)),
          nrow = 1,
          align = c("hv"))

# Annotate the figure by adding a common labels
cor_clock_cor <- annotate_figure(clock_cord_cor_gg,
                bottom = text_grob("DNAmGA estimated by the Knight Clock (weeks)", size = 12), top = text_grob("Correlation Cord blood Clocks \n", size = 14))
```

```{r}
png(file="Results/Figures/corClocks/cord.png", width= 3600, height=2100, res=480)
annotate_figure(clock_cord_cor_gg,
                bottom = text_grob("DNAmGA estimated by the Knight Clock (weeks)", size = 12))
dev.off()
```

[to the top](#top)  

## correlation placenta clocks {#corPlacentaClocks}  

```{r}
cor_placenta_clocks_itu <- ggscatter(Data_Placenta_ITU, x = "DNAmGA_Mayne", y = "DNAmGA_Lee", 
          add = "reg.line", conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = "pearson",
          xlab = "DNAmGA estimated by the Mayne Clock", ylab = "DNAmGA estimated by the Lee Clock (weeks)", subtitle="ITU (n=486)")+
   stat_cor(label.x = 25, label.y=43,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(30,44), breaks = seq(30,44, by=2))+
  scale_x_continuous(limits = c(25,40), breaks = seq(26,40, by=2))


cor_placenta_clocks_predo <- ggscatter(Data_PREDO_EPICplacenta, x = "DNAmGA_Mayne", y = "DNAmGA_Lee", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "DNAmGA estimated by the Lee Clock", ylab = "DNAmGA estimated by the Mayne Clock", subtitle="PREDO (n=139)")+
   stat_cor(label.x = 26, label.y=43,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(30,44), breaks = seq(30,44, by=2))+
  scale_x_continuous(limits = c(26,36), breaks = seq(26,36, by=2))

clock_placenta_cor_gg <- ggarrange(
          cor_placenta_clocks_itu +
           theme(legend.position="none", plot.margin = margin(r = 0.2) ),
          cor_placenta_clocks_predo +
               theme(axis.text.y = element_blank(),
                     axis.ticks.y = element_blank(), axis.title.y = element_blank(), plot.margin = margin(r = 0.2, l = 0.2)),
          nrow = 1,
          align = c("hv"))

# Annotate the figure by adding a common labels
pla_clock_cor <- annotate_figure(clock_placenta_cor_gg,
                bottom = text_grob("DNAmGA estimated by the Mayne Clock (weeks)", size = 12), top = text_grob("Correlation Placenta Clocks \n", size = 14))

```

```{r}
png(file="Results/Figures/corClocks/placenta.png", width= 2400, height=1400, res=320)
annotate_figure(clock_placenta_cor_gg,
                bottom = text_grob("DNAmGA estimated by the Mayne Clock (weeks)", size = 12))
dev.off()
```

```{r}
ggscatter(Data_CVS_ITU, x = "Gestational_Age_Weeks", y = "delta_Lee", 
          add = "reg.line", conf.int = TRUE, 
          cor.coef = TRUE, cor.method = "pearson",
          xlab = "gestational age (weeks)", ylab = "delta Lee", title="Correlation CVS gestational age deviance (ITU)")
```

[to the top](#top)

# EAAR Descriptive
## ITU: Visualization EAAR {#plotsEAARITU}  


```{r}
ifelse(!dir.exists(file.path(getwd(), "Results/Figures/EAAR_descriptive")), dir.create(file.path(getwd(), "Results/Figures/EAAR_descriptive")), FALSE)
```

**CVS**
```{r}
EAARCVS <- ggplot(Data_CVS_ITU, aes(x= gestage_at_CVS_weeks, y= EAAR_Lee, label=Sample_Name))+
  geom_point() +geom_text(aes(label=Sample_Name),hjust=0, vjust=0)+
  xlab("gestational age at sampling (weeks)")+
  xlim(5,20)+
  ylim(-10,10)+
  geom_line(y=0, linetype="dashed")+
  ylab("epigenetic age acceleration residuals \n(Lee clock)")

EAARCVS_sex <- Data_CVS_ITU[!is.na(Data_CVS_ITU$EAAR_Lee), ] %>%
  group_by(Child_Sex) %>%
  mutate(outlier = ifelse(is_outlier(EAAR_Lee), Sample_Name, as.numeric(NA))) %>%
  ggplot(., aes(x = Child_Sex, y = EAAR_Lee)) +
    geom_boxplot() +
    geom_text(size=2.5, aes(label = outlier), na.rm = TRUE, hjust=-0.3)+
  xlab("Child sex")+
  ylab("epigenetic age acceleration residuals \n(Lee clock)")+
  geom_hline(aes(yintercept=0))

EAARCVS_boxplot <- ggplot(Data_CVS_ITU, aes(x=EAAR_Lee))+ geom_histogram(binwidth=0.1)+ labs(x="epigenetic age acceleration residuals (Lee clock)", y = "Count (N = 200)")

cowplot::plot_grid(EAARCVS, EAARCVS_sex, EAARCVS_boxplot)

length(na.omit(Data_CVS_ITU$EAAR_Lee))
# note that 65 rows were removed because they are NA in EAARVS (no ethnicity info)
```

```{r}
deltaCVS_boxplot <- ggplot(Data_CVS_ITU, aes(x=delta_Lee))+ geom_histogram(binwidth=0.1)+ labs(x="epigenetic age acceleration delta (Lee clock)", y = "Count (N = 200)")
#deltaCVS_boxplot
```


```{r}
png(file="Results/Figures/EAAR_descriptive/CVS.png",width=2200, height=1400, res=300)
ggplot(Data_CVS_ITU, aes(x=EAAR_Lee))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Lee clock)", y = "Count (n = 200)")+
theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
dev.off()
```

**Cordblood**
```{r}
EAARCord <- ggplot(Data_Cord_ITU, aes(x= Gestational_Age_Weeks, y= EAAR_Bohlin, label=Sample_Name))+
  geom_point() +geom_text(aes(label=Sample_Name),hjust=0, vjust=0)+
  xlab("gestational age at birth (weeks)")+
  xlim(25,50)+
  ylim(-10,10)+
  geom_line(y=0, linetype="dashed")+
  ylab("epigenetic age acceleration residuals \nBohlin clock")

EAARCord_sex <- Data_Cord_ITU[!is.na(Data_Cord_ITU$EAAR_Bohlin), ] %>%
  group_by(Child_Sex) %>%
  mutate(outlier = ifelse(is_outlier(EAAR_Bohlin), Sample_Name, as.numeric(NA))) %>%
  ggplot(., aes(x =Child_Sex, y = EAAR_Bohlin)) +
    geom_boxplot() +
    geom_text(size=2.5,aes(label = outlier), na.rm = TRUE, hjust = -0.3)+
  xlab("Child sex")+
  ylab("epigenetic age acceleration residuals \nBohlin clock")+
  geom_hline(aes(yintercept=0))

EAARCord_boxplot <- ggplot(Data_Cord_ITU, aes(x=EAAR_Bohlin))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Bohlin clock)", y = "Count (N = 395)")

cowplot::plot_grid(EAARCord, EAARCord_sex, EAARCord_boxplot)
length(na.omit(Data_Cord_ITU$EAAR_Bohlin))
```

```{r}
png(file="Results/Figures/EAAR_descriptive/Cord.png",width=2200, height=1400, res=300)
ggplot(Data_Cord_ITU, aes(x=EAAR_Bohlin))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Bohlin clock)", y = "Count (n = 395)")+
theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
dev.off()
```
```{r}
deltaCord_boxplot <- ggplot(Data_Cord_ITU, aes(x=delta_Bohlin))+ geom_histogram(binwidth=0.1)+ labs(x="delta (Bohlin clock)", y = "Count (N = 395)")
#deltaCord_boxplot
```


**Placenta**
```{r}
EAARPlacenta <- ggplot(Data_Placenta_ITU, aes(x= Gestational_Age_Weeks, y= EAAR_Lee, label=Sample_Name))+
  geom_point() +geom_text(aes(label=Sample_Name),hjust=0, vjust=0)+
  xlab("gestational age at birth (weeks)")+
  xlim(25,50)+
  ylim(-10,10)+
  geom_line(y=0, linetype="dashed")+
  ylab("epigenetic age acceleration residuals \nLee clock")

EAARPlacenta_sex <- Data_Placenta_ITU[!is.na(Data_Placenta_ITU$EAAR_Lee), ] %>%
  group_by(Child_Sex) %>%
  mutate(outlier = ifelse(is_outlier(EAAR_Lee), Sample_Name, as.numeric(NA))) %>%
  ggplot(., aes(x = Child_Sex, y = EAAR_Lee)) +
    geom_boxplot() +
    geom_text(size=2.5,aes(label = outlier), na.rm = TRUE, hjust = -0.3)+
  xlab("Child sex")+
  ylab("epigenetic age acceleration residuals \nLee clock")+
  geom_hline(aes(yintercept=0))

EAARPlacenta_boxplot <- ggplot(Data_Placenta_ITU, aes(x=EAAR_Lee))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Lee clock)", y = "Count (N = 439)")

cowplot::plot_grid(EAARPlacenta, EAARPlacenta_sex, EAARPlacenta_boxplot)
length(na.omit(Data_Placenta_ITU$EAAR_Lee))
```

```{r}
png("Results/Figures/EAAR_descriptive/Placenta.png", width=2200, height=1400, res=300)
ggplot(Data_Placenta_ITU, aes(x=EAAR_Lee))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Lee clock)", y = "Count (n = 439)")+
theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
dev.off()
```
```{r}
deltaPlacenta_boxplot <- ggplot(Data_Placenta_ITU, aes(x=delta_Lee))+ geom_histogram(binwidth=0.1)+ labs(x="delta (Lee clock)", y = "Count (N = 486)")
deltaPlacenta_boxplot 
```

[to the top](#top)  

## PREDO: Visualization EAAR {#plotsEAARPREDO}  

**450K Cordblood**
```{r}
EAARCord450K <- ggplot(Data_PREDO_450Kcord, aes(x= Gestational_Age, y= EAAR_Bohlin, label=Sample_Name))+
  geom_point() +geom_text(aes(label=Sample_Name),hjust=0, vjust=0)+
  xlab("gestational age at birth (weeks)")+
  xlim(25,50)+
  ylim(-15,15)+
  geom_line(y=0, linetype="dashed")+
  ylab("epigenetic age acceleration residuals \nBohlin clock")

EAARCord450K_sex <- Data_PREDO_450Kcord[!is.na(Data_PREDO_450Kcord$EAAR_Bohlin), ] %>%
  group_by(Child_Sex) %>%
  mutate(outlier = ifelse(is_outlier(EAAR_Bohlin), Sample_Name, as.numeric(NA))) %>%
  ggplot(., aes(x = Child_Sex, y = EAAR_Bohlin)) +
    geom_boxplot() +
    geom_text(size=2.5,aes(label = outlier), na.rm = TRUE, hjust = -0.3)+
  xlab("Child sex")+
  ylab("epigenetic age acceleration residuals \nBohlin clock")+
  geom_hline(aes(yintercept=0))

EAARCord450K_boxplot <- ggplot(Data_PREDO_450Kcord, aes(x=EAAR_Bohlin))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Bohlin clock)", y = "Count (N = 785)")

#cowplot::plot_grid(EAARCord450K, EAARCord450K_sex, EAARCord450K_boxplot)
length(na.omit(Data_PREDO_450Kcord$EAAR_Bohlin))
```

```{r}
png("Results/Figures/EAAR_descriptive/Cord450K_PREDO.png", width=2200, height=1400, res=300)
ggplot(Data_PREDO_450Kcord, aes(x=EAAR_Bohlin))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Bohlin clock)", y = "Count (n = 785)")+
theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
dev.off()
```


**EPIC Cordblood**
```{r}
EAARCordEPIC <- ggplot(Data_PREDO_EPICcord, aes(x= Gestational_Age, y= EAAR_Bohlin, label=Sample_Name))+
  geom_point() +geom_text(aes(label=Sample_Name),hjust=0, vjust=0)+
  xlab("gestational age at birth (weeks)")+
  xlim(30,45)+
  ylim(-15,15)+
  geom_line(y=0, linetype="dashed")+
  ylab("epigenetic age acceleration residuals \nBohlin clock")

EAARCordEPIC_sex <- Data_PREDO_EPICcord[!is.na(Data_PREDO_EPICcord$EAAR_Bohlin), ] %>%
  group_by(Child_Sex) %>%
  mutate(outlier = ifelse(is_outlier(EAAR_Bohlin), Sample_Name, as.numeric(NA))) %>%
  ggplot(., aes(x = Child_Sex, y = EAAR_Bohlin)) +
    geom_boxplot() +
    geom_text(aes(label = outlier), na.rm = TRUE, hjust = -0.3)+
  xlab("Child sex")+
  ylab("epigenetic age acceleration residuals \nBohlin clock")+
  geom_hline(aes(yintercept=0))

EAARCordEPIC_boxplot <- ggplot(Data_PREDO_EPICcord, aes(x=EAAR_Bohlin))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Bohlin clock)", y = "Count (N = 146)")

#cowplot::plot_grid(EAARCordEPIC, EAARCordEPIC_sex, EAARCordEPIC_boxplot)
length(na.omit(Data_PREDO_EPICcord$EAAR_Bohlin))
```

```{r}
png("Results/Figures/EAAR_descriptive/CordEPIC_PREDO.png", width=2200, height=1400, res=300)
ggplot(Data_PREDO_EPICcord, aes(x=EAAR_Bohlin))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Bohlin clock)", y = "Count (n = 146)")+
theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
dev.off()
```

**EPIC Placenta**
```{r}
EAARPlacentaEPIC <- ggplot(Data_PREDO_EPICplacenta, aes(x= Gestational_Age, y= EAAR_Lee, label=Sample_Name))+
  geom_point() +geom_text(aes(label=Sample_Name),hjust=0, vjust=0)+
  xlab("gestational age at birth (weeks)")+
  xlim(30,45)+
  ylim(-15,15)+
  geom_line(y=0, linetype="dashed")+
  ylab("epigenetic age acceleration residuals \nLee clock")

EAARPlacentaEPIC_sex <- Data_PREDO_EPICplacenta[!is.na(Data_PREDO_EPICplacenta$EAAR_Lee),] %>%
  group_by(Child_Sex) %>%
  #mutate(outlier = ifelse(is_outlier(EAAR_Lee), Sample_Name, as.numeric(NA))) %>%
  ggplot(., aes(x = Child_Sex, y = EAAR_Lee)) +
    geom_boxplot() +
    #geom_text(size=2.5, aes(label = outlier), na.rm = TRUE, hjust = -0.3)+
  xlab("Child sex")+
  ylab("epigenetic age acceleration residuals \nLee clock")+
  geom_hline(aes(yintercept=0))

EAARPlacentaEPIC_boxplot <- ggplot(Data_PREDO_EPICplacenta, aes(x=EAAR_Lee))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Lee clock)", y = "Count (N = 118)")

#cowplot::plot_grid(EAARPlacentaEPIC, EAARPlacentaEPIC_sex, EAARPlacentaEPIC_boxplot)
length(na.omit(Data_PREDO_EPICplacenta$EAAR_Lee))
```

```{r}
png("Results/Figures/EAAR_descriptive/PlacentaEPIC_PREDO.png", width=2200, height=1400, res=300)
ggplot(Data_PREDO_EPICplacenta, aes(x=EAAR_Lee))+ geom_histogram(binwidth=0.1)+ labs(x="EAAR (Lee clock)", y = "Count (n = 118)")+
theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
dev.off()
```

[to the top](#top)

# Single Tissue Models

```{r}
ifelse(!dir.exists(file.path(getwd(), "InputData/Data_ElasticNets/")), dir.create(file.path(getwd(), "InputData/Data_ElasticNets/")), FALSE)
```

```{r}
ifelse(!dir.exists(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/")), dir.create(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/")), FALSE)
```

```{r}
ifelse(!dir.exists(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/Outcome_main/")), dir.create(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/Outcome_main/")), FALSE)
```

```{r}
ifelse(!dir.exists(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/Outcome_add/")), dir.create(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/Outcome_add/")), FALSE)
```

```{r}
ifelse(!dir.exists(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol")), dir.create(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol")), FALSE)
```

```{r}
ifelse(!dir.exists(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split")), dir.create(file.path(getwd(), "Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split")), FALSE)
```

```{r}
ifelse(!dir.exists(file.path(getwd(), "Results/Tables/")), dir.create(file.path(getwd(), "Results/Tables/")), FALSE)
```

```{r}
rm(list = setdiff(ls(), lsf.str()))
```


**ITU**

## Cord blood elastic net {#elasticnetCordITU}  
main model, without alcohol variable

```{r}
# in case you want to start from here
load("InputData/ClockCalculationsInput/Reg_Input_Data_Cord_ITU_EAAR_noNa_n.Rdata")
```


```{r}
yrc_mat_ITU_Cord_n <- matrix(Reg_Input_Data_Cord_ITU_EAAR_noNa_n$EAAR_Bohlin)
xrc_mat_ITU_Cord_n <- model.matrix( ~ . - EAAR_Bohlin, data = Reg_Input_Data_Cord_ITU_EAAR_noNa_n)[, -1]
yrc_mat_ITU_scaled_Cord_n <- scale(yrc_mat_ITU_Cord_n)
xrc_mat_ITU_scaled_Cord_n <- scale(xrc_mat_ITU_Cord_n)
```

<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Cord_ITU_n <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_ITU_scaled_Cord_n), replace = TRUE) -->
<!--     ensr(xrc_mat_ITU_scaled_Cord_n[rws, ], yrc_mat_ITU_scaled_Cord_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!--   end_time <- Sys.time() -->
<!--   end_time - start_time -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Cord_ITU_n, file="InputData/Data_ElasticNets/bootstraps_Cord_ITU_n_1000.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/bootstraps_Cord_ITU_n_1000.Rdata")
```

first get a summary of all ensr objects
```{r}
summaries_Cord_ITU_n <-
  bootstraps_Cord_ITU_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Cord_ITU_n
```

The summary method for ensr objects returns a data.table with values of λ, α, the mean cross-validation error cvm, and the number of non-zero coefficients. The l_index is the list index of the ensr object associated with the noted α value.

For each bootstrap, look at the number of non-zero coefficients and the minimum cvm for this number of non-zero coefficients:
```{r}
summaries_Cord_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()+
  ggplot2::labs(x="\nnzero", y="cvm\n")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))+
  ggplot2::theme_bw()
  
```
in the "standard" procedure, the preferable model is defined as the model with the minimum cvm (nzero, alpha, lambda etc. are selected from this)

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstraps_Cord.png", width=2200, height=1400, res=400)
summaries_Cord_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()+
  ggplot2::labs(x="\nnzero", y="cvm\n")+
  ggplot2::theme(text = element_text(size = 18), axis.title.x= element_text(size=20), axis.title.y= element_text(size=20))+
  ggplot2::theme_bw()
dev.off()
```

Now a look at the coefficients
build a data.table with columns to store the coefficient values for the models with smallest cvm by number of non-zero coefficients (and bootstrap).

<!-- ```{r, warning=FALSE} -->
<!-- # lowest cvm by bootstrap and nzero -->
<!-- pm_Cord_ITU_n <- summaries_Cord_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] -->
<!-- pm2_Cord_ITU_n <- NULL -->

<!-- for(i in as.integer(seq(1, nrow(pm_Cord_ITU_n), by = 1))) { -->
<!--   pm2_Cord_ITU_n <- rbind(pm2_Cord_ITU_n, -->
<!--                cbind(pm_Cord_ITU_n[i, ], -->
<!--                t(as.matrix(coef(bootstraps_Cord_ITU_n[[pm_Cord_ITU_n[i, bootstrap]]][[pm_Cord_ITU_n[i, l_index]]], s = pm_Cord_ITU_n[i, lambda]))) -->
<!--                ) -->
<!--   ) -->
<!-- } -->

<!-- pm2_Cord_ITU_n -->
<!-- ``` -->


<!-- ```{r} -->
<!-- # save "preferable models" -->
<!-- save(pm2_Cord_ITU_n, file="InputData/Data_ElasticNets/pm2_Cord_ITU_n.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/pm2_Cord_ITU_n.Rdata")
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap
```

look how often a particular variable is associated with a non-zero coefficient in a model with a given number of non-zero coefficients (over all bootstraps)

```{r}
csummary_Cord_ITU_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                               list(pm2_Cord_ITU_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"), by = nzero]
                                    ,
                                    pm2_Cord_ITU_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                    pm2_Cord_ITU_n[, .(median_cvm = median(cvm)), by = nzero]
                               ))[order(nzero)]

csummary_Cord_ITU_n
```


plot the results, in the following graphic the size and color of the points in the top plot indicate how often the variable is in the model with nzero non-zero coefficents

```{r}
g1_Cord_ITU_n <-
  csummary_Cord_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex", "birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor\n", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
  

g2_Cord_ITU_n <-
  csummary_Cord_ITU_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "nzero")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Cord_ITU_n, g2_Cord_ITU_n, ncol = 1)

```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstrapModels_Cord.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Cord_ITU_n, g2_Cord_ITU_n, ncol = 1)
dev.off()
```
```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_Cord.png", width=2800, height=1400, res=400)
g1_Cord_ITU_n
dev.off()
```


```{r}
elbow_finder(csummary_Cord_ITU_n$nzero, csummary_Cord_ITU_n$median_cvm)

nzero_indices_Cord <- data.frame(t(elbow_finder(csummary_Cord_ITU_n$nzero, csummary_Cord_ITU_n$median_cvm)))
colnames(nzero_indices_Cord) <- c("x", "y")
rownames(nzero_indices_Cord) <- NULL
```
```{r}
nzero_final_cord_itu <- 9
```

look at models with 9 non-zero coefficient.
```{r}
csummary_Cord_ITU_n[nzero %in% nzero_final_cord_itu]
```

```{r}
nonzero_choose_Cord <- ggplot2::ggplot(csummary_Cord_ITU_n) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_Cord, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median cvm over bootstraps\n")+
  ggplot2::xlab("\nnumber of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[14], yend = median_cvm[14], colour = "segment"), data = csummary_Cord_ITU_n, show.legend = F)+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
  
nonzero_choose_Cord
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/nzero_choose_Cord.png", width=2200, height=1400, res=400)
nonzero_choose_Cord
dev.off()
```
look at models with 9 non-zero coefficients.
filter for cut-off 75% -> which variables occur in more than 75% of models.

```{r}
summary_Cord_ITU_n_finalnzero <- csummary_Cord_ITU_n[nzero %in% nzero_final_cord_itu]
sig_var_names_Cord_ITU_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_Cord_ITU_n_finalnzero[,!c("nzero", "mean_cvm", "median_cvm")]) %>% colnames()
colnames(summary_Cord_ITU_n_finalnzero) <- c("non-zero", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "mean cvm", "median cvm")
summary_Cord_ITU_n_finalnzeroT <- as.data.frame(t(summary_Cord_ITU_n_finalnzero[,-c("non-zero", "median cvm", "mean cvm")]))
summary_Cord_ITU_n_finalnzeroT$variable <- rownames(summary_Cord_ITU_n_finalnzeroT)
rownames(summary_Cord_ITU_n_finalnzeroT) <- NULL
names(summary_Cord_ITU_n_finalnzeroT)[names(summary_Cord_ITU_n_finalnzeroT) == 'V1'] <- 'percent'
summary_Cord_ITU_n_finalnzeroT <- summary_Cord_ITU_n_finalnzeroT[order(summary_Cord_ITU_n_finalnzeroT$percent),]

summary_Cord_ITU_n_finalnzeroT$number <- seq(1, length(summary_Cord_ITU_n_finalnzeroT$variable))
```

```{r, fig.width=8}
perc_vars_Cord_ITU_n <- 
  ggplot(summary_Cord_ITU_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("\n% occurence in models with nzero coefficients = 9    ")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("predictor\n")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

perc_vars_Cord_ITU_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Cord_ITU_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])

```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/varsPercent_Cord.png", width=2900, height=1400, res=400)
perc_vars_Cord_ITU_n
dev.off()
```

A metric of interest could be the width of the confidence intervals about a bootstrapped estimate of the coefficient, when the coefficient is non-zero:
  
```{r}
pm2_Cord_ITU_n_coef <-
  dcast(pm2_Cord_ITU_n[,
                       as.list(unlist(
                         lapply(.SD,
                                function(x) {
                                  y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                  list("non_zero" = 100 * mean(x != 0),
                                       lcl = y[1],
                                       ucl = y[2],
                                       width = diff(y),
                                       median = median(x[x!= 0]))
                                }))),
                       .SDcols = c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"),
                       by = nzero][order(nzero)] %>%
          melt(id.var = "nzero") %>%
          .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
          .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
          .[nzero ==nzero_final_cord_itu], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_Cord_ITU_n_coef <-
  pm2_Cord_ITU_n_coef[match(c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"), pm2_Cord_ITU_n_coef$variable),]
pm2_Cord_ITU_n_coef$variable <- factor(pm2_Cord_ITU_n_coef$variabl, levels=unique(pm2_Cord_ITU_n_coef$variable))

## NOTE: median is used here instead of mean
# make frame for only significant variables:
pm2_Cord_ITU_n_datable <- dcast(pm2_Cord_ITU_n[,
                                               as.list(unlist(
                                                 lapply(.SD,
                                                        function(x) {
                                                          y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                                          list("non_zero" = 100 * mean(x != 0),
                                                               lcl = y[1],
                                                               ucl = y[2],
                                                               width = diff(y),
                                                               median = median(x[x!= 0]))
                                                        }))),
                                               .SDcols = c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"),
                                               by = nzero][order(nzero)] %>%
                                  melt(id.var = "nzero") %>%
                                  .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
                                  .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
                                  # print %>%
                                  .[nzero == nzero_final_cord_itu & variable %in% sig_var_names_Cord_ITU_n_finalnzero], nzero+ variable ~ metric, value.var="value")

pm2_Cord_ITU_n_datable

```

```{r}
write_xlsx(pm2_Cord_ITU_n_coef,"Results/Tables/CoefficientsModel_Cord.xlsx")
```


```{r}
sig_vars_Cord_ITU_n <-
  pm2_Cord_ITU_n_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x="nzero")+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf("%0.2f", round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients = 9", color="%")

```

```{r}
coef_Cord_ITU_n <- 
  ggplot(pm2_Cord_ITU_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Cord_ITU_n 
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/coef_Cord.png", width=2800, height=1400, res=400)
coef_Cord_ITU_n 
dev.off()
```


```{r}
p1 <-
  csummary_Cord_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor\n", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
  ggplot(pm2_Cord_ITU_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
   ggtitle("nzero = 9")+
  theme_bw()+
 theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_coef_Cord.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()
```


get the beta values

```{r}
### Code for only including "significant variables" in the beta vector, based on VIP (>75% not-zero in bootstraps)

# get median beta values of the 1000 bootstraps for the model with 9 non-zero coefficients
Beta_hat_s_cord_n <- matrix(miscTools::colMedians(pm2_Cord_ITU_n[nzero == nzero_final_cord_itu, .SD, .SDcols = c("(Intercept)",sig_var_names_Cord_ITU_n_finalnzero)]), ncol = 1)
# intenept and variable beta values
# NOTE that median is used here
rownames(Beta_hat_s_cord_n) <- c("Intercept", sig_var_names_Cord_ITU_n_finalnzero)

Beta_Cord_ITU_n <- Beta_hat_s_cord_n
```

```{r}
save(Beta_Cord_ITU_n, file="InputData/Data_ElasticNets/Beta_Cord_ITU_n.Rdata")
```

[to the top](#top)

## Cord blood elastic net {#elasticnetCordITU_a}  
additional model, with alcohol variable

```{r}
# in case you want to start from here
load("InputData/ClockCalculationsInput/Reg_Input_Data_Cord_ITU_EAAR_noNa_wa.Rdata")
```

```{r}
yrc_mat_ITU_Cord_wa <- matrix(Reg_Input_Data_Cord_ITU_EAAR_noNa_wa$EAAR_Bohlin)
xrc_mat_ITU_Cord_wa <- model.matrix( ~ . - EAAR_Bohlin, data = Reg_Input_Data_Cord_ITU_EAAR_noNa_wa)[, -1]
yrc_mat_ITU_scaled_Cord_wa <- scale(yrc_mat_ITU_Cord_wa)
xrc_mat_ITU_scaled_Cord_wa <- scale(xrc_mat_ITU_Cord_wa)
```

<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Cord_ITU_wa <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_ITU_scaled_Cord_wa), replace = TRUE) -->
<!--     ensr(xrc_mat_ITU_scaled_Cord_wa[rws, ], yrc_mat_ITU_scaled_Cord_wa[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!--   end_time <- Sys.time() -->
<!--   end_time - start_time -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Cord_ITU_wa, file="InputData/Data_ElasticNets/bootstraps_Cord_ITU_wa_1000.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/bootstraps_Cord_ITU_wa_1000.Rdata")
```

```{r}
summaries_Cord_ITU_wa <-
  bootstraps_Cord_ITU_wa %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Cord_ITU_wa
```

```{r}
summaries_Cord_ITU_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/bootstraps_Cord.png", width=800, height=600)
summaries_Cord_ITU_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```


<!-- ```{r, warning=FALSE} -->
<!-- # lowest cvm by bootstrap and nzero -->
<!-- pm_Cord_ITU_wa <- summaries_Cord_ITU_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] -->
<!-- pm2_Cord_ITU_wa <- NULL -->

<!-- for(i in as.integer(seq(1, nrow(pm_Cord_ITU_wa), by = 1))) { -->
<!--   pm2_Cord_ITU_wa <- rbind(pm2_Cord_ITU_wa, -->
<!--                cbind(pm_Cord_ITU_wa[i, ], -->
<!--                t(as.matrix(coef(bootstraps_Cord_ITU_wa[[pm_Cord_ITU_wa[i, bootstrap]]][[pm_Cord_ITU_wa[i, l_index]]], s = pm_Cord_ITU_wa[i, lambda]))) -->
<!--                ) -->
<!--   ) -->
<!-- } -->

<!-- pm2_Cord_ITU_wa -->
<!-- ``` -->


<!-- ```{r} -->
<!-- # save "preferable models" -->
<!-- save(pm2_Cord_ITU_wa, file="InputData/Data_ElasticNets/pm2_Cord_ITU_wa.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/pm2_Cord_ITU_wa.Rdata")
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap
```

```{r}
csummary_Cord_ITU_wa <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                               list(pm2_Cord_ITU_wa[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes", "maternal_alcohol_useyes"), by = nzero]
                                    ,
                                    pm2_Cord_ITU_wa[, .(mean_cvm = mean(cvm)), by = nzero],
                                    pm2_Cord_ITU_wa[, .(median_cvm = median(cvm)), by = nzero]
                               ))[order(nzero)]

csummary_Cord_ITU_wa
```


```{r}
g1_Cord_ITU_wa <-
  csummary_Cord_ITU_wa %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex", "birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking", "maternal alcohol use"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

g2_Cord_ITU_wa <-
  csummary_Cord_ITU_wa %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Cord_ITU_wa, g2_Cord_ITU_wa, ncol = 1)

```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/bootstrapModels_Cord.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Cord_ITU_wa, g2_Cord_ITU_wa, ncol = 1)
dev.off()
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/Model_Cord.png", width=2800, height=1400, res=400)
g1_Cord_ITU_wa
dev.off()
```

```{r}
elbow_finder(csummary_Cord_ITU_wa$nzero, csummary_Cord_ITU_wa$median_cvm)

nzero_indices_Cord <- data.frame(t(elbow_finder(csummary_Cord_ITU_wa$nzero, csummary_Cord_ITU_wa$median_cvm)))
colnames(nzero_indices_Cord) <- c("x", "y")
rownames(nzero_indices_Cord) <- NULL
```
```{r}
nzero_final_cord_wa <- 7
```

look at models with final non-zero coefficient.
```{r}
csummary_Cord_ITU_wa[nzero %in% nzero_final_cord_wa]
```

```{r}
nonzero_choose_Cord <- ggplot2::ggplot(csummary_Cord_ITU_wa) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_Cord, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median of minimum cross-validation errors over bootstraps")+
  ggplot2::xlab("number of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[15], yend = median_cvm[15], colour = "segment"), data = csummary_Cord_ITU_wa, show.legend = F)

nonzero_choose_Cord
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/nzero_choose_Cord.png", width=1600, height=1400, res=300)
nonzero_choose_Cord
dev.off()
```

```{r}
summary_Cord_ITU_wa_finalnzero <- csummary_Cord_ITU_wa[nzero %in% nzero_final_cord_wa]
sig_var_names_Cord_ITU_wa_finalnzero <- Filter(function(x) any(x > 0.75), summary_Cord_ITU_wa_finalnzero[,!c("nzero", "mean_cvm", "median_cvm")]) %>% colnames()
colnames(summary_Cord_ITU_wa_finalnzero) <- c("non-zero", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)", "mean cvm", "median cvm")
summary_Cord_ITU_wa_finalnzeroT <- as.data.frame(t(summary_Cord_ITU_wa_finalnzero[,-c("non-zero", "median cvm", "mean cvm")]))
summary_Cord_ITU_wa_finalnzeroT$variable <- rownames(summary_Cord_ITU_wa_finalnzeroT)
rownames(summary_Cord_ITU_wa_finalnzeroT) <- NULL
names(summary_Cord_ITU_wa_finalnzeroT)[names(summary_Cord_ITU_wa_finalnzeroT) == 'V1'] <- 'percent'
summary_Cord_ITU_wa_finalzeroT <- summary_Cord_ITU_wa_finalnzeroT[order(summary_Cord_ITU_wa_finalnzeroT$percent),]

summary_Cord_ITU_wa_finalnzeroT$number <- seq(1, length(summary_Cord_ITU_wa_finalnzeroT$variable))
```

```{r, fig.width=8}
perc_vars_Cord_ITU_wa <- 
  ggplot(summary_Cord_ITU_wa_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("% occurence in models with nzero coefficients = 8")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("variable")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()

perc_vars_Cord_ITU_wa

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Cord_ITU_wa_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])

```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/varsPercent_Cord.png", width=1100, height=1400, res=300)
perc_vars_Cord_ITU_wa
dev.off()
```

  
```{r}
pm2_Cord_ITU_wa_coef <-
  dcast(pm2_Cord_ITU_wa[,
                       as.list(unlist(
                         lapply(.SD,
                                function(x) {
                                  y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                  list("non_zero" = 100 * mean(x != 0),
                                       lcl = y[1],
                                       ucl = y[2],
                                       width = diff(y),
                                       median = median(x[x!= 0]))
                                }))),
                       .SDcols = c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes", "maternal_alcohol_useyes"),
                       by = nzero][order(nzero)] %>%
          melt(id.var = "nzero") %>%
          .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
          .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
          .[nzero ==nzero_final_cord_wa], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_Cord_ITU_wa_coef <-
  pm2_Cord_ITU_wa_coef[match(c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes", "maternal_alcohol_useyes"), pm2_Cord_ITU_wa_coef$variable),]
pm2_Cord_ITU_wa_coef$variable <- factor(pm2_Cord_ITU_wa_coef$variabl, levels=unique(pm2_Cord_ITU_wa_coef$variable))

## NOTE: median is used here instead of mean
# make frame for only significant variables:
pm2_Cord_ITU_wa_datable <- dcast(pm2_Cord_ITU_wa[,
                                               as.list(unlist(
                                                 lapply(.SD,
                                                        function(x) {
                                                          y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                                          list("non_zero" = 100 * mean(x != 0),
                                                               lcl = y[1],
                                                               ucl = y[2],
                                                               width = diff(y),
                                                               median = median(x[x!= 0]))
                                                        }))),
                                               .SDcols = c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes", "maternal_alcohol_useyes"),
                                               by = nzero][order(nzero)] %>%
                                  melt(id.var = "nzero") %>%
                                  .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
                                  .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
                                  # print %>%
                                  .[nzero == nzero_final_cord_wa & variable %in% sig_var_names_Cord_ITU_wa_finalnzero], nzero+ variable ~ metric, value.var="value")

pm2_Cord_ITU_wa_datable

```

```{r}
sig_vars_Cord_ITU_wa <-
  pm2_Cord_ITU_wa_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x="nzero")+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf("%0.2f", round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients = 8", color="%")

```

```{r}
coef_Cord_ITU_wa <- 
  ggplot(pm2_Cord_ITU_wa_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Cord_ITU_wa 
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/coef_Cord.png", width=2800, height=1400, res=400)
coef_Cord_ITU_wa 
dev.off()
```


```{r}
p1 <-
  csummary_Cord_ITU_wa %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
  ggplot(pm2_Cord_ITU_wa_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  ggtitle("nzero = 7")+
  theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/Model_coef_Cord.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()
```

get the beta values

```{r}
### Code for only including "significant variables" in the beta vector, based on VIP (>75% not-zero in bootstraps)

# get median beta values of the 1000 bootstraps for the model with 7 non-zero coefficients
Beta_hat_s_cord_wa <- matrix(miscTools::colMedians(pm2_Cord_ITU_wa[nzero == nzero_final_cord_wa, .SD, .SDcols = c("(Intercept)",sig_var_names_Cord_ITU_wa_finalnzero)]), ncol = 1)
# intenept and variable beta values
# NOTE that median is used here
rownames(Beta_hat_s_cord_wa) <- c("Intercept", sig_var_names_Cord_ITU_wa_finalnzero)

Beta_Cord_ITU_wa <- Beta_hat_s_cord_wa
```

```{r}
save(Beta_Cord_ITU_wa, file="InputData/Data_ElasticNets/Beta_Cord_ITU_wa.Rdata")
```

[to the top](#top)


## CVS elastic net {#elasticnetCVSITU}  
main model, without alcohol variable


```{r}
# in case you want to start from here
load("InputData/ClockCalculationsInput/Reg_Input_Data_CVS_ITU_EAAR_n_noNa.Rdata")
```


```{r}
yrc_mat_ITU_CVS_n <- matrix(Reg_Input_Data_CVS_ITU_EAAR_n_noNa$EAAR_Lee)
xrc_mat_ITU_CVS_n <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_CVS_ITU_EAAR_n_noNa)[, -1]
yrc_mat_ITU_scaled_CVS_n <- scale(yrc_mat_ITU_CVS_n)
xrc_mat_ITU_scaled_CVS_n <- scale(xrc_mat_ITU_CVS_n)
```


<!-- set seed -->

<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=FALSE} -->
<!-- nboot = 1000 -->

<!-- bootstraps_CVS_ITU_n <- replicate(nboot,{ -->
<!--   rws <- sample(1:nrow(xrc_mat_ITU_scaled_CVS_n), replace = TRUE); -->
<!--   ensr(xrc_mat_ITU_scaled_CVS_n[rws, ], yrc_mat_ITU_scaled_CVS_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100,nfolds=10,alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0))}, simplify = FALSE) -->

<!-- ``` -->


<!-- ```{r} -->
<!-- # save bootstrap object -->
<!-- save(bootstraps_CVS_ITU_n, file="InputData/Data_ElasticNets/bootstraps_CVS_ITU_n_1000.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/bootstraps_CVS_ITU_n_1000.Rdata")
```


```{r}
summaries_CVS_ITU_n <-
  bootstraps_CVS_ITU_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_CVS_ITU_n
```


```{r}
summaries_CVS_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstraps_CVS.png", width=800, height=600)
summaries_CVS_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```


<!-- ```{r, warning=FALSE} -->
<!-- # lowest cvm by bootstrap and nzero -->
<!-- pm_CVS_ITU_n <- summaries_CVS_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] -->
<!-- pm2_CVS_ITU_n <- NULL -->

<!-- for(i in as.integer(seq(1, nrow(pm_CVS_ITU_n), by = 1))) { -->
<!--   pm2_CVS_ITU_n <- rbind(pm2_CVS_ITU_n, -->
<!--                cbind(pm_CVS_ITU_n[i, ], -->
<!--                t(as.matrix(coef(bootstraps_CVS_ITU_n[[pm_CVS_ITU_n[i, bootstrap]]][[pm_CVS_ITU_n[i, l_index]]], s = pm_CVS_ITU_n[i, lambda]))) -->
<!--                ) -->
<!--   ) -->
<!-- } -->

<!-- pm2_CVS_ITU_n -->
<!-- ``` -->


<!-- ```{r} -->
<!-- # save "preferable models" -->
<!-- save(pm2_CVS_ITU_n, file="InputData/Data_ElasticNets/pm2_CVS_ITU_n.Rdata") -->
<!-- ``` -->



```{r}
load("InputData/Data_ElasticNets/pm2_CVS_ITU_n.Rdata")
```


```{r}
csummary_CVS_ITU_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                              list(pm2_CVS_ITU_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Gestational_Age_Weeks", "Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"), by = nzero]
                                   ,
                                   pm2_CVS_ITU_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                   pm2_CVS_ITU_n[, .(median_cvm = median(cvm)), by = nzero]
                              ))[order(nzero)]

csummary_CVS_ITU_n
```


```{r, fig.width=8, fig.heigth=8}
g1_CVS_ITU_n <-
  csummary_CVS_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

g2_CVS_ITU_n <-
  csummary_CVS_ITU_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))


gridExtra::grid.arrange(g1_CVS_ITU_n, g2_CVS_ITU_n, ncol = 1)

# note: not a big difference if mean/median cvm is used
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstrapModels_CVS.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_CVS_ITU_n, g2_CVS_ITU_n, ncol = 1)
dev.off()
```
```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_CVS.png", width=2800, height=1400, res=400)
g1_CVS_ITU_n
dev.off()
```


```{r}
elbow_finder(csummary_CVS_ITU_n$nzero[-1], csummary_CVS_ITU_n$median_cvm[-1])
nzero_indices_CVS <- data.frame(t(elbow_finder(csummary_CVS_ITU_n$nzero[-1], csummary_CVS_ITU_n$median_cvm[-1])))
colnames(nzero_indices_CVS) <- c("x", "y")
rownames(nzero_indices_CVS) <- NULL
```
```{r}
nzero_final_CVS <- 8
```

```{r}
nonzero_choose_CVS <- ggplot2::ggplot(csummary_CVS_ITU_n) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_CVS, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median of minimum cross-validation errors over bootstraps")+
  ggplot2::xlab("number of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[15], yend = median_cvm[15], colour = "segment"), data = csummary_CVS_ITU_n, show.legend = F)

nonzero_choose_CVS
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/nzero_choose_CVS.png", width=1600, height=1400, res=300)
nonzero_choose_CVS
dev.off()
```

```{r}
summary_CVS_ITU_n_finalnzero <- csummary_CVS_ITU_n[nzero %in% nzero_final_CVS]
sig_var_names_CVS_ITU_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_CVS_ITU_n_finalnzero[,!c("nzero", "mean_cvm", "median_cvm")]) %>% colnames()
colnames(summary_CVS_ITU_n_finalnzero) <- c("non-zero", "gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "mean cvm", "median cvm")
summary_CVS_ITU_n_finalnzeroT <- as.data.frame(t(summary_CVS_ITU_n_finalnzero[,-c("non-zero", "median cvm", "mean cvm")]))
summary_CVS_ITU_n_finalnzeroT$variable <- rownames(summary_CVS_ITU_n_finalnzeroT)
rownames(summary_CVS_ITU_n_finalnzeroT) <- NULL
names(summary_CVS_ITU_n_finalnzeroT)[names(summary_CVS_ITU_n_finalnzeroT) == 'V1'] <- 'percent'
summary_CVS_ITU_n_finalnzeroT <- summary_CVS_ITU_n_finalnzeroT[order(summary_CVS_ITU_n_finalnzeroT$percent),]

summary_CVS_ITU_n_finalnzeroT$number <- seq(1, length(summary_CVS_ITU_n_finalnzeroT$variable))
```


```{r, fig.width=8}
perc_vars_CVS_ITU_n <- 
ggplot(summary_CVS_ITU_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
geom_point()+ geom_line()+
ylab("% occurence in models with nzero coefficients = 9")+
scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
xlab("variable")+
coord_flip()+
geom_hline(yintercept=0.75, linetype="dotted")+
theme_bw()

perc_vars_CVS_ITU_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_CVS_ITU_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])

```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/varsPercent_CVS.png", width=1800, height=1400, res=300)
perc_vars_CVS_ITU_n
dev.off()
```


```{r}
pm2_CVS_ITU_n_coef <-
dcast(pm2_CVS_ITU_n[,
as.list(unlist(
lapply(.SD,
function(x) {
y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
list("non_zero" = 100 * mean(x != 0),
lcl = y[1],
ucl = y[2],
width = diff(y),
median = median(x[x!= 0]))
}))),
.SDcols = c("Gestational_Age_Weeks", "Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"),
by = nzero][order(nzero)] %>%
melt(id.var = "nzero") %>%
.[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
.[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
.[nzero ==nzero_final_CVS], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_CVS_ITU_n_coef <-
pm2_CVS_ITU_n_coef[match(c("Gestational_Age_Weeks", "Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"), pm2_CVS_ITU_n_coef$variable),]
pm2_CVS_ITU_n_coef$variable <- factor(pm2_CVS_ITU_n_coef$variabl, levels=unique(pm2_CVS_ITU_n_coef$variable))

```

```{r}
write_xlsx(pm2_CVS_ITU_n_coef,"Results/Tables/CoefficientsModel_CVS.xlsx")
```

```{r}
sig_vars_CVS_ITU_n <-
pm2_CVS_ITU_n_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x="nzero")+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf("%0.2f", round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients = 9", color="%")

```

```{r}
coef_CVS_ITU_n <- 
ggplot(pm2_CVS_ITU_n_coef, aes(y = variable, x=median))+
geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
scale_alpha(guide = 'none')+
scale_size(guide = 'none')+
geom_point()+
geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
scale_y_discrete(labels= c("gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
geom_vline(xintercept=0, linetype="dashed")+
theme_bw()+
theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_CVS_ITU_n 
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/coef_CVS.png", width=2800, height=1400, res=400)
coef_CVS_ITU_n 
dev.off()
```

```{r}
g1_CVS_ITU_n <-
  csummary_CVS_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 20), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")

coef_CVS_ITU_n <- 
ggplot(pm2_CVS_ITU_n_coef, aes(y = variable, x=median))+
geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
scale_alpha(guide = 'none')+
scale_size(guide = 'none')+
geom_point()+
geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
labs(y="", x = "median & 95% CI of coefficient (over bootstraps)", color="%")+
#ggtitle("nzero = 8")+
scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
scale_y_discrete(labels= c("gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
geom_vline(xintercept=0, linetype="dashed")+
theme_bw()+
theme(text = element_text(size = 20), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
#plot.title = element_text(size=15)

```

Plot:
```{r}
p1 <-
  csummary_CVS_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")

p2 <- 
ggplot(pm2_CVS_ITU_n_coef, aes(y = variable, x=median))+
geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
scale_alpha(guide = 'none')+
scale_size(guide = 'none')+
geom_point()+
geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
ggtitle("nzero = 8")+
scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
scale_y_discrete(labels= c("gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
geom_vline(xintercept=0, linetype="dashed")+
theme_bw()+
theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_coef_CVS.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()
```

[to the top](#top)

## CVS elastic net {#elasticnetCVSITU_a}  
additional model, with alcohol variable

```{r}
# in case you want to start from here
load("InputData/ClockCalculationsInput/Reg_Input_Data_CVS_ITU_EAAR_wa_noNa.Rdata")
```


```{r}
yrc_mat_ITU_CVS_wa <- matrix(Reg_Input_Data_CVS_ITU_EAAR_wa_noNa$EAAR_Lee)
xrc_mat_ITU_CVS_wa <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_CVS_ITU_EAAR_wa_noNa)[, -1]
yrc_mat_ITU_scaled_CVS_wa <- scale(yrc_mat_ITU_CVS_wa)
xrc_mat_ITU_scaled_CVS_wa <- scale(xrc_mat_ITU_CVS_wa)
```


<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=FALSE} -->
<!-- nboot = 1000 -->

<!-- start_time <- Sys.time() -->
<!-- bootstraps_CVS_ITU_wa <- replicate(nboot, { -->
<!--   rws <- sample(1:nrow(xrc_mat_ITU_scaled_CVS_wa), replace = TRUE) -->
<!--   ensr(xrc_mat_ITU_scaled_CVS_wa[rws, ], yrc_mat_ITU_scaled_CVS_wa[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!-- }, -->
<!-- simplify = FALSE) -->

<!-- end_time <- Sys.time() -->
<!-- end_time - start_time -->
<!-- # generates a list of length 100, each a unique call to ensr (= also a list of cv.glmnet objects, which is determined by the length of alphas) -->
<!-- # nlambda = number of lambda values, default 100 -->
<!-- # alpha: sequence of alphas to use, ensr will add length(alphas)-1 additional values (midpoints) in the construction of the alpha-lambda grid to search -->
<!-- # nfold= number of folds (default 10) for internal cv to fit hyperparameters -->

<!-- ``` -->


<!-- ```{r} -->
<!-- # save bootstrap object -->
<!-- save(bootstraps_CVS_ITU_wa, file="InputData/Data_ElasticNets/bootstraps_CVS_ITU_wa_1000.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/bootstraps_CVS_ITU_wa_1000.Rdata")
```


```{r}
summaries_CVS_ITU_wa <-
  bootstraps_CVS_ITU_wa %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_CVS_ITU_wa
```

```{r}
summaries_CVS_ITU_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/bootstraps_CVS.png", width=800, height=600)
summaries_CVS_ITU_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```


<!-- ```{r, warning=FALSE} -->
<!-- # lowest cvm by bootstrap and nzero -->
<!-- pm_CVS_ITU_wa <- summaries_CVS_ITU_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] -->
<!-- pm2_CVS_ITU_wa <- NULL -->

<!-- for(i in as.integer(seq(1, nrow(pm_CVS_ITU_wa), by = 1))) { -->
<!--   pm2_CVS_ITU_wa <- rbind(pm2_CVS_ITU_wa, -->
<!--                cbind(pm_CVS_ITU_wa[i, ], -->
<!--                t(as.matrix(coef(bootstraps_CVS_ITU_wa[[pm_CVS_ITU_wa[i, bootstrap]]][[pm_CVS_ITU_wa[i, l_index]]], s = pm_CVS_ITU_wa[i, lambda]))) -->
<!--                ) -->
<!--   ) -->
<!-- } -->

<!-- pm2_CVS_ITU_wa -->
<!-- ``` -->


<!-- ```{r} -->
<!-- # save "preferable models" -->
<!-- save(pm2_CVS_ITU_wa, file="InputData/Data_ElasticNets/pm2_CVS_ITU_wa.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/pm2_CVS_ITU_wa.Rdata")
```


```{r}
csummary_CVS_ITU_wa <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                              list(pm2_CVS_ITU_wa[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Gestational_Age_Weeks", "Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"
, "maternal_alcohol_useyes"), by = nzero]
                                   ,
                                   pm2_CVS_ITU_wa[, .(mean_cvm = mean(cvm)), by = nzero],
                                   pm2_CVS_ITU_wa[, .(median_cvm = median(cvm)), by = nzero]
                              ))[order(nzero)]

csummary_CVS_ITU_wa
```


```{r, fig.width=8, fig.heigth=8}
g1_CVS_ITU_wa <-
  csummary_CVS_ITU_wa %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("gestage at birth", "child sex", "birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking", "maternal alcohol use"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
  

g2_CVS_ITU_wa <-
  csummary_CVS_ITU_wa %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_CVS_ITU_wa, g2_CVS_ITU_wa, ncol = 1)

# note: not a big difference if mean/median cvm is used
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/Model_CVS.png", width=2800, height=1400, res=400)
g1_CVS_ITU_wa
dev.off()
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/bootstrapModels_CVS.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_CVS_ITU_wa, g2_CVS_ITU_wa, ncol = 1)
dev.off()
```


```{r}
elbow_finder(csummary_CVS_ITU_wa$nzero, csummary_CVS_ITU_wa$median_cvm)
nzero_indices_CVS <- data.frame(t(elbow_finder(csummary_CVS_ITU_wa$nzero, csummary_CVS_ITU_wa$median_cvm)))
colnames(nzero_indices_CVS) <- c("x", "y")
rownames(nzero_indices_CVS) <- NULL
```

```{r}
nonzero_choose_CVS <- ggplot2::ggplot(csummary_CVS_ITU_wa) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_CVS, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median of minimum cross-validation errors over bootstraps")+
  ggplot2::xlab("number of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[16], yend = median_cvm[16], colour = "segment"), data = csummary_CVS_ITU_wa, show.legend = F)

nonzero_choose_CVS
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/nzero_choose_CVS.png", width=1600, height=1400, res=300)
nonzero_choose_CVS
dev.off()
```

```{r}
nzero_final_CVS_wa <- 10
```

```{r}
csummary_CVS_ITU_wa[nzero %in% nzero_final_CVS_wa]
```


```{r}
summary_CVS_ITU_wa_finalnzero <- csummary_CVS_ITU_wa[nzero %in% nzero_final_CVS_wa]
sig_var_names_CVS_ITU_wa_finalnzero <- Filter(function(x) any(x > 0.75), summary_CVS_ITU_wa_finalnzero[,!c("nzero", "mean_cvm", "median_cvm")]) %>% colnames()
colnames(summary_CVS_ITU_wa_finalnzero) <- c("non-zero", "gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol (yes)", "mean cvm", "median cvm")
summary_CVS_ITU_wa_finalnzeroT <- as.data.frame(t(summary_CVS_ITU_wa_finalnzero[,-c("non-zero", "median cvm", "mean cvm")]))
summary_CVS_ITU_wa_finalnzeroT$variable <- rownames(summary_CVS_ITU_wa_finalnzeroT)
rownames(summary_CVS_ITU_wa_finalnzeroT) <- NULL
names(summary_CVS_ITU_wa_finalnzeroT)[names(summary_CVS_ITU_wa_finalnzeroT) == 'V1'] <- 'percent'
summary_CVS_ITU_wa_finalnzeroT <- summary_CVS_ITU_wa_finalnzeroT[order(summary_CVS_ITU_wa_finalnzeroT$percent),]

summary_CVS_ITU_wa_finalnzeroT$number <- seq(1, length(summary_CVS_ITU_wa_finalnzeroT$variable))
```


```{r, fig.width=8}
perc_vars_CVS_ITU_wa <- 
ggplot(summary_CVS_ITU_wa_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
geom_point()+ geom_line()+
ylab("% occurence in models with nzero coefficients = 8")+
scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
xlab("variable")+
coord_flip()+
geom_hline(yintercept=0.75, linetype="dotted")+
theme_bw()

perc_vars_CVS_ITU_wa

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_CVS_ITU_wa_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])

```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/varsPercent_CVS.png", width=1100, height=1400, res=300)
perc_vars_CVS_ITU_wa
dev.off()
```


```{r}
pm2_CVS_ITU_wa_coef <-
dcast(pm2_CVS_ITU_wa[,
as.list(unlist(
lapply(.SD,
function(x) {
y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
list("non_zero" = 100 * mean(x != 0),
lcl = y[1],
ucl = y[2],
width = diff(y),
median = median(x[x!= 0]))
}))),
.SDcols = c("Gestational_Age_Weeks", "Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes", "maternal_alcohol_useyes"),
by = nzero][order(nzero)] %>%
melt(id.var = "nzero") %>%
.[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
.[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
.[nzero == nzero_final_CVS_wa], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_CVS_ITU_wa_coef <-
pm2_CVS_ITU_wa_coef[match(c("Gestational_Age_Weeks", "Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes", "maternal_alcohol_useyes"), pm2_CVS_ITU_wa_coef$variable),]
pm2_CVS_ITU_wa_coef$variable <- factor(pm2_CVS_ITU_wa_coef$variabl, levels=unique(pm2_CVS_ITU_wa_coef$variable))

## NOTE: median is used here instead of mean
# make frame for only significant variables:
pm2_CVS_ITU_wa_datable <- dcast(pm2_CVS_ITU_wa[,
as.list(unlist(
lapply(.SD,
function(x) {
y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
list("non_zero" = 100 * mean(x != 0),
lcl = y[1],
ucl = y[2],
width = diff(y),
median = median(x[x!= 0]))
}))),
.SDcols = c("Gestational_Age_Weeks", "Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes", "maternal_alcohol_useyes"),
by = nzero][order(nzero)] %>%
melt(id.var = "nzero") %>%
.[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
.[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
# print %>%
.[nzero == nzero_final_CVS_wa & variable %in% sig_var_names_CVS_ITU_wa_finalnzero], nzero+ variable ~ metric, value.var="value")

pm2_CVS_ITU_wa_datable 
```

```{r}
sig_vars_CVS_ITU_wa <-
pm2_CVS_ITU_wa_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x="nzero")+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf("%0.2f", round(median, digits=2)), size=30),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients = 8", color="%")

```

```{r}
coef_CVS_ITU_wa <- 
  ggplot(pm2_CVS_ITU_wa_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.5,0.4), breaks=c(-.5, -.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

coef_CVS_ITU_wa
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/coef_CVS.png", width=2800, height=1400, res=400)
coef_CVS_ITU_wa 
dev.off()
```

```{r}
p1 <-
  g1_CVS_ITU_wa <-
  csummary_CVS_ITU_wa %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:15, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
  ggplot(pm2_CVS_ITU_wa_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  ggtitle("nzero = 10")+
  scale_x_continuous(limits=c(-0.5,0.4), breaks=c(-.5, -.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("gestage at birth", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), , plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/Model_coef_CVS.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()
```


## Placenta elastic net {#elasticnetPlacentaITU}  
main model, without alcohol variable

```{r}
# in case you want to start from here
load("InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_ITU_EAAR_noNa_n.Rdata")
```


```{r}
yrc_mat_ITU_Placenta_n <- matrix(Reg_Input_Data_Placenta_ITU_EAAR_noNa_n$EAAR_Lee)
xrc_mat_ITU_Placenta_n <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_Placenta_ITU_EAAR_noNa_n)[, -1]
yrc_mat_ITU_scaled_Placenta_n <- scale(yrc_mat_ITU_Placenta_n)
xrc_mat_ITU_scaled_Placenta_n <- scale(xrc_mat_ITU_Placenta_n)
```

<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Placenta_ITU_n <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_ITU_scaled_Placenta_n), replace = TRUE) -->
<!--     ensr(xrc_mat_ITU_scaled_Placenta_n[rws, ], yrc_mat_ITU_scaled_Placenta_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!--   end_time <- Sys.time() -->
<!--   end_time - start_time -->

<!--   #Time difference of 3.159319 hours -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Placenta_ITU_n, file="InputData/Data_ElasticNets/bootstraps_Placenta_ITU_n_1000.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/bootstraps_Placenta_ITU_n_1000.Rdata")
```


```{r}
summaries_Placenta_ITU_n <-
  bootstraps_Placenta_ITU_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Placenta_ITU_n
```


```{r}
summaries_Placenta_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstraps_Placenta.png", width=800, height=600)
summaries_Placenta_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```


<!-- ```{r, warning=FALSE} -->
<!-- # lowest cvm by bootstrap and nzero -->
<!-- pm_Placenta_ITU_n <- summaries_Placenta_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] -->
<!-- pm2_Placenta_ITU_n <- NULL -->

<!-- for(i in as.integer(seq(1, nrow(pm_Placenta_ITU_n), by = 1))) { -->
<!--   pm2_Placenta_ITU_n <- rbind(pm2_Placenta_ITU_n, -->
<!--                cbind(pm_Placenta_ITU_n[i, ], -->
<!--                t(as.matrix(coef(bootstraps_Placenta_ITU_n[[pm_Placenta_ITU_n[i, bootstrap]]][[pm_Placenta_ITU_n[i, l_index]]], s = pm_Placenta_ITU_n[i, lambda]))) -->
<!--                ) -->
<!--   ) -->
<!-- } -->

<!-- pm2_Placenta_ITU_n -->
<!-- ``` -->


<!-- ```{r} -->
<!-- # save "preferable models" -->
<!-- save(pm2_Placenta_ITU_n, file="InputData/Data_ElasticNets/pm2_Placenta_ITU_n.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/pm2_Placenta_ITU_n.Rdata")
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap
```


```{r}
csummary_Placenta_ITU_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                                   list(pm2_Placenta_ITU_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"), by = nzero]
                                        ,
                                        pm2_Placenta_ITU_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                        pm2_Placenta_ITU_n[, .(median_cvm = median(cvm)), by = nzero]
                                   ))[order(nzero)]

csummary_Placenta_ITU_n
```


```{r}
g1_Placenta_ITU_n <-
  csummary_Placenta_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex", "birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

g2_Placenta_ITU_n <-
  csummary_Placenta_ITU_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))



gridExtra::grid.arrange(g1_Placenta_ITU_n, g2_Placenta_ITU_n, ncol = 1)
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstrapModels_Placenta.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Placenta_ITU_n, g2_Placenta_ITU_n, ncol = 1)
dev.off()
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_Placenta.png", width=2800, height=1400, res=400)
g1_Placenta_ITU_n
dev.off()
```


```{r}
elbow_finder(csummary_Placenta_ITU_n$nzero, csummary_Placenta_ITU_n$median_cvm)

nzero_indices_Placenta <- data.frame(t(elbow_finder(csummary_Placenta_ITU_n$nzero, csummary_Placenta_ITU_n$median_cvm)))
colnames(nzero_indices_Placenta) <- c("x", "y")
rownames(nzero_indices_Placenta) <- NULL
```


```{r}
nzero_final_placenta_itu <- 7
```


```{r}
summary_Placenta_ITU_n_finalnzero <- csummary_Placenta_ITU_n[nzero %in% nzero_final_placenta_itu]
sig_var_names_Placenta_ITU_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_Placenta_ITU_n_finalnzero[,!c("nzero", "mean_cvm", "median_cvm")]) %>% colnames()
colnames(summary_Placenta_ITU_n_finalnzero) <- c("non-zero","child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "mean cvm", "median cvm")
summary_Placenta_ITU_n_finalnzeroT <- as.data.frame(t(summary_Placenta_ITU_n_finalnzero[,-c("non-zero", "median cvm", "mean cvm")]))
summary_Placenta_ITU_n_finalnzeroT$variable <- rownames(summary_Placenta_ITU_n_finalnzeroT)
rownames(summary_Placenta_ITU_n_finalnzeroT) <- NULL
names(summary_Placenta_ITU_n_finalnzeroT)[names(summary_Placenta_ITU_n_finalnzeroT) == 'V1'] <- 'percent'
summary_Placenta_ITU_n_finalnzeroT <- summary_Placenta_ITU_n_finalnzeroT[order(summary_Placenta_ITU_n_finalnzeroT$percent),]

summary_Placenta_ITU_n_finalnzeroT$number <- seq(1, length(summary_Placenta_ITU_n_finalnzeroT$variable))
```

```{r, fig.width=8}
perc_vars_Placenta_ITU_n <- 
  ggplot(summary_Placenta_ITU_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("% occurence in models with nzero coefficients = 4")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("variable")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()

perc_vars_Placenta_ITU_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Placenta_ITU_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/varsPercent_Placenta.png", width=1800, height=1400, res=300)
perc_vars_Placenta_ITU_n
dev.off()
```
```{r}
pm2_Placenta_ITU_n_coef <-
  dcast(pm2_Placenta_ITU_n[,
                        as.list(unlist(
                          lapply(.SD,
                                 function(x) {
                                   y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                   list("non_zero" = 100 * mean(x != 0),
                                        lcl = y[1],
                                        ucl = y[2],
                                        width = diff(y),
                                        median = median(x[x!= 0]))
                                 }))),
                        .SDcols = c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"),
                        by = nzero][order(nzero)] %>%
          melt(id.var = "nzero") %>%
          .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
          .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
          .[nzero == nzero_final_placenta_itu], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_Placenta_ITU_n_coef <-
  pm2_Placenta_ITU_n_coef[match(c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"), pm2_Placenta_ITU_n_coef$variable),]
pm2_Placenta_ITU_n_coef$variable <- factor(pm2_Placenta_ITU_n_coef$variabl, levels=unique(pm2_Placenta_ITU_n_coef$variable))

```

```{r}
write_xlsx(pm2_Placenta_ITU_n_coef,"Results/Tables/CoefficientsModel_Placenta.xlsx")
```


```{r}
sig_vars_Placenta_ITU_n <-
  pm2_Placenta_ITU_n_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x="nzero")+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf("%0.2f", round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients = 7", color="%")

```

```{r}
coef_Placenta_ITU_n <- 
  ggplot(pm2_Placenta_ITU_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Placenta_ITU_n 
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/coef_Placenta.png", width=2800, height=1400, res=400)
coef_Placenta_ITU_n
dev.off()
```

```{r}
p1 <-
  csummary_Placenta_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size =17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
  ggplot(pm2_Placenta_ITU_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  ggtitle("nzero = 7")+
  theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_coef_Placenta.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()
```

[to the top](#top) 

## Placenta elastic net {#elasticnetPlacentaITU_a}  
additional model, with alcohol variable

```{r}
# in case you want to start from here
load("InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa.Rdata")
```


```{r}
yrc_mat_ITU_Placenta_wa <- matrix(Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa$EAAR_Lee)
xrc_mat_ITU_Placenta_wa <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_Placenta_ITU_EAAR_noNa_wa)[, -1]
yrc_mat_ITU_scaled_Placenta_wa <- scale(yrc_mat_ITU_Placenta_wa)
xrc_mat_ITU_scaled_Placenta_wa <- scale(xrc_mat_ITU_Placenta_wa)
```

<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Placenta_ITU_wa <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_ITU_scaled_Placenta_wa), replace = TRUE) -->
<!--     ensr(xrc_mat_ITU_scaled_Placenta_wa[rws, ], yrc_mat_ITU_scaled_Placenta_wa[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!--   end_time <- Sys.time() -->
<!--   end_time - start_time -->

<!--   #Time difference of 3.159319 hours -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Placenta_ITU_wa, file="InputData/Data_ElasticNets/bootstraps_Placenta_ITU_wa_1000.Rdata") -->
<!-- ``` -->



```{r}
load("InputData/Data_ElasticNets/bootstraps_Placenta_ITU_wa_1000.Rdata")
```


```{r}
summaries_Placenta_ITU_wa <-
  bootstraps_Placenta_ITU_wa %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Placenta_ITU_wa
```


```{r}
summaries_Placenta_ITU_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/bootstraps_Placenta.png", width=800, height=600)
summaries_Placenta_ITU_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```


<!-- ```{r, warning=FALSE} -->
<!-- # lowest cvm by bootstrap and nzero -->
<!-- pm_Placenta_ITU_wa <- summaries_Placenta_ITU_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] -->
<!-- pm2_Placenta_ITU_wa <- NULL -->

<!-- for(i in as.integer(seq(1, nrow(pm_Placenta_ITU_wa), by = 1))) { -->
<!--   pm2_Placenta_ITU_wa <- rbind(pm2_Placenta_ITU_wa, -->
<!--                cbind(pm_Placenta_ITU_wa[i, ], -->
<!--                t(as.matrix(coef(bootstraps_Placenta_ITU_wa[[pm_Placenta_ITU_wa[i, bootstrap]]][[pm_Placenta_ITU_wa[i, l_index]]], s = pm_Placenta_ITU_wa[i, lambda]))) -->
<!--                ) -->
<!--   ) -->
<!-- } -->

<!-- pm2_Placenta_ITU_wa -->
<!-- ``` -->


<!-- ```{r} -->
<!-- # save "preferable models" -->
<!-- save(pm2_Placenta_ITU_wa, file="InputData/Data_ElasticNets/pm2_Placenta_ITU_wa.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/pm2_Placenta_ITU_wa.Rdata")
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap
```


```{r}
csummary_Placenta_ITU_wa <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                                  list(pm2_Placenta_ITU_wa[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes", "maternal_alcohol_useyes"), by = nzero]
                                       ,
                                       pm2_Placenta_ITU_wa[, .(mean_cvm = mean(cvm)), by = nzero],
                                       pm2_Placenta_ITU_wa[, .(median_cvm = median(cvm)), by = nzero]
                                  ))[order(nzero)]

csummary_Placenta_ITU_wa
```


```{r}
g1_Placenta_ITU_wa <-
  csummary_Placenta_ITU_wa %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex", "birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking", "maternal alcohol use"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

g2_Placenta_ITU_wa <-
  csummary_Placenta_ITU_wa %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Placenta_ITU_wa, g2_Placenta_ITU_wa, ncol = 1)

```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/bootstrapModels_Placenta.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Placenta_ITU_wa, g2_Placenta_ITU_wa, ncol = 1)
dev.off()
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/Model_Placenta.png", width=2800, height=1400, res=400)
g1_Placenta_ITU_wa
dev.off()
```


```{r}
elbow_finder(csummary_Placenta_ITU_wa$nzero, csummary_Placenta_ITU_wa$median_cvm)

nzero_indices_Placenta <- data.frame(t(elbow_finder(csummary_Placenta_ITU_wa$nzero, csummary_Placenta_ITU_wa$median_cvm)))
colnames(nzero_indices_Placenta) <- c("x", "y")
rownames(nzero_indices_Placenta) <- NULL
```

```{r}
nzero_final_itu_placenta_wa <- 6
```

```{r}
csummary_Placenta_ITU_wa[nzero %in% nzero_final_itu_placenta_wa]
```


```{r}
nonzero_choose_Placenta <- ggplot2::ggplot(csummary_Placenta_ITU_wa) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_Placenta, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median of minimum cross-validation errors over bootstraps")+
  ggplot2::xlab("number of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[15], yend = median_cvm[15], colour = "segment"), data = csummary_Placenta_ITU_wa, show.legend = F)

nonzero_choose_Placenta
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/nzero_choose_Placenta.png", width=1600, height=1400, res=300)
nonzero_choose_Placenta
dev.off()
```

```{r}
summary_Placenta_ITU_wa_finalnzero <- csummary_Placenta_ITU_wa[nzero %in% nzero_final_itu_placenta_wa]
sig_var_names_Placenta_ITU_wa_finalnzero <- Filter(function(x) any(x > 0.75), summary_Placenta_ITU_wa_finalnzero[,!c("nzero", "mean_cvm", "median_cvm")]) %>% colnames()
colnames(summary_Placenta_ITU_wa_finalnzero) <- c("non-zero", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)", "mean cvm", "median cvm")
summary_Placenta_ITU_wa_finalnzeroT <- as.data.frame(t(summary_Placenta_ITU_wa_finalnzero[,-c("non-zero", "median cvm", "mean cvm")]))
summary_Placenta_ITU_wa_finalnzeroT$variable <- rownames(summary_Placenta_ITU_wa_finalnzeroT)
rownames(summary_Placenta_ITU_wa_finalnzeroT) <- NULL
names(summary_Placenta_ITU_wa_finalnzeroT)[names(summary_Placenta_ITU_wa_finalnzeroT) == 'V1'] <- 'percent'
summary_Placenta_ITU_wa_finalzeroT <- summary_Placenta_ITU_wa_finalnzeroT[order(summary_Placenta_ITU_wa_finalnzeroT$percent),]

summary_Placenta_ITU_wa_finalnzeroT$number <- seq(1, length(summary_Placenta_ITU_wa_finalnzeroT$variable))
```

```{r, fig.width=8}
perc_vars_Placenta_ITU_wa <- 
  ggplot(summary_Placenta_ITU_wa_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("% occurence in models with nzero coefficients = 8")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("variable")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()

perc_vars_Placenta_ITU_wa

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Placenta_ITU_wa_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])

```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/varsPercent_Placenta.png", width=1100, height=1400, res=300)
perc_vars_Placenta_ITU_wa
dev.off()
```


  
```{r}
pm2_Placenta_ITU_wa_coef <-
  dcast(pm2_Placenta_ITU_wa[,
                        as.list(unlist(
                          lapply(.SD,
                                 function(x) {
                                   y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                   list("non_zero" = 100 * mean(x != 0),
                                        lcl = y[1],
                                        ucl = y[2],
                                        width = diff(y),
                                        median = median(x[x!= 0]))
                                 }))),
                        .SDcols = c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes", "maternal_alcohol_useyes"),
                        by = nzero][order(nzero)] %>%
          melt(id.var = "nzero") %>%
          .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
          .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
          .[nzero == nzero_final_itu_placenta_wa], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_Placenta_ITU_wa_coef <-
  pm2_Placenta_ITU_wa_coef[match(c("Child_Sexfemale", "Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes", "maternal_alcohol_useyes"), pm2_Placenta_ITU_wa_coef$variable),]
pm2_Placenta_ITU_wa_coef$variable <- factor(pm2_Placenta_ITU_wa_coef$variabl, levels=unique(pm2_Placenta_ITU_wa_coef$variable))
```

```{r}
sig_vars_Placenta_ITU_wa <-
  pm2_Placenta_ITU_wa_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x="nzero")+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf("%0.2f", round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients = 7", color="%")

```

```{r}
coef_Placenta_ITU_wa <- 
  ggplot(pm2_Placenta_ITU_wa_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Placenta_ITU_wa 
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/coef_Placenta.png", width=2800, height=1400, res=400)
coef_Placenta_ITU_wa
dev.off()
```

```{r}
p1 <-
   csummary_Placenta_ITU_wa %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
  ggplot(pm2_Placenta_ITU_wa_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  ggtitle("nzero = 6")+
  theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/Model_coef_Placenta.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()
```

[to the top](#top)

## Placenta elastic net splitted by sex {#elasticnetPlacentaITU_s}  
model without alcohol variable, but splitted by sex

### males
  
```{r}
# in case you want to start from here
load("InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_n.Rdata")
Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_n$Child_Sex <- NULL
```


```{r}
yrc_mat_ITU_Placenta_male_n <- matrix(Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_n$EAAR_Lee)
xrc_mat_ITU_Placenta_male_n <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_Placenta_male_ITU_EAAR_noNa_n)[, -1]
yrc_mat_ITU_scaled_Placenta_male_n <- scale(yrc_mat_ITU_Placenta_male_n)
xrc_mat_ITU_scaled_Placenta_male_n <- scale(xrc_mat_ITU_Placenta_male_n)
```

<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Placenta_male_ITU_n <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_ITU_scaled_Placenta_male_n), replace = TRUE) -->
<!--     ensr(xrc_mat_ITU_scaled_Placenta_male_n[rws, ], yrc_mat_ITU_scaled_Placenta_male_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Placenta_male_ITU_n, file="InputData/Data_ElasticNets/bootstraps_Placenta_male_ITU_n_1000.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/bootstraps_Placenta_male_ITU_n_1000.Rdata")
```


```{r}
summaries_Placenta_male_ITU_n <-
  bootstraps_Placenta_male_ITU_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Placenta_male_ITU_n
```

```{r}
summaries_Placenta_male_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/bootstraps_Placenta_MALE.png", width=800, height=600)
summaries_Placenta_male_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```


<!-- ```{r, warning=FALSE} -->
<!-- # lowest cvm by bootstrap and nzero -->
<!-- pm_Placenta_male_ITU_n <- summaries_Placenta_male_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] -->
<!-- pm2_Placenta_male_ITU_n <- NULL -->

<!-- for(i in as.integer(seq(1, nrow(pm_Placenta_male_ITU_n), by = 1))) { -->
<!--   pm2_Placenta_male_ITU_n <- rbind(pm2_Placenta_male_ITU_n, -->
<!--                cbind(pm_Placenta_male_ITU_n[i, ], -->
<!--                t(as.matrix(coef(bootstraps_Placenta_male_ITU_n[[pm_Placenta_male_ITU_n[i, bootstrap]]][[pm_Placenta_male_ITU_n[i, l_index]]], s = pm_Placenta_male_ITU_n[i, lambda]))) -->
<!--                ) -->
<!--   ) -->
<!-- } -->

<!-- pm2_Placenta_male_ITU_n -->
<!-- ``` -->


<!-- ```{r} -->
<!-- # save "preferable models" -->
<!-- save(pm2_Placenta_male_ITU_n, file="InputData/Data_ElasticNets/pm2_Placenta_male_ITU_n.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/pm2_Placenta_male_ITU_n.Rdata")
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap
```


```{r}
csummary_Placenta_male_ITU_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                              list(pm2_Placenta_male_ITU_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"), by = nzero]
                                   ,
                                   pm2_Placenta_male_ITU_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                   pm2_Placenta_male_ITU_n[, .(median_cvm = median(cvm)), by = nzero]
                              ))[order(nzero)]

csummary_Placenta_male_ITU_n
```


```{r}
g1_Placenta_male_ITU_n <-
  csummary_Placenta_male_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

g2_Placenta_male_ITU_n <-
  csummary_Placenta_male_ITU_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))


gridExtra::grid.arrange(g1_Placenta_male_ITU_n, g2_Placenta_male_ITU_n, ncol = 1)

```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/bootstrapModels_Placenta_male.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Placenta_male_ITU_n, g2_Placenta_male_ITU_n, ncol = 1)
dev.off()
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/Model_Placenta_male.png", width=2800, height=1400, res=400)
g1_Placenta_male_ITU_n
dev.off()
```


```{r}
elbow_finder(csummary_Placenta_male_ITU_n$nzero[-13], csummary_Placenta_male_ITU_n$median_cvm[-13])

nzero_indices_Cord <- data.frame(t(elbow_finder(csummary_Placenta_male_ITU_n$nzero[-13], csummary_Placenta_male_ITU_n$median_cvm[-13])))
colnames(nzero_indices_Cord) <- c("x", "y")
rownames(nzero_indices_Cord) <- NULL
```
```{r}
nzero_final_placenta_male <- 5
```


```{r}
csummary_Placenta_male_ITU_n[nzero %in% nzero_final_placenta_male]
```


```{r}
summary_Placenta_male_ITU_n_finalnzero <- csummary_Placenta_male_ITU_n[nzero %in% nzero_final_placenta_male]
sig_var_names_Placenta_male_ITU_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_Placenta_male_ITU_n_finalnzero[,!c("nzero", "mean_cvm", "median_cvm")]) %>% colnames()
colnames(summary_Placenta_male_ITU_n_finalnzero) <- c("non-zero", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "mean cvm", "median cvm")
summary_Placenta_male_ITU_n_finalnzeroT <- as.data.frame(t(summary_Placenta_male_ITU_n_finalnzero[,-c("non-zero", "median cvm", "mean cvm")]))
summary_Placenta_male_ITU_n_finalnzeroT$variable <- rownames(summary_Placenta_male_ITU_n_finalnzeroT)
rownames(summary_Placenta_male_ITU_n_finalnzeroT) <- NULL
names(summary_Placenta_male_ITU_n_finalnzeroT)[names(summary_Placenta_male_ITU_n_finalnzeroT) == 'V1'] <- 'percent'
summary_Placenta_male_ITU_n_finalnzeroT <- summary_Placenta_male_ITU_n_finalnzeroT[order(summary_Placenta_male_ITU_n_finalnzeroT$percent),]

summary_Placenta_male_ITU_n_finalnzeroT$number <- seq(1, length(summary_Placenta_male_ITU_n_finalnzeroT$variable))
```

```{r, fig.width=8}
perc_vars_Placenta_male_ITU_n <- 
  ggplot(summary_Placenta_male_ITU_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("% occurence in models with nzero coefficients = 2")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("variable")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()

perc_vars_Placenta_male_ITU_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Placenta_male_ITU_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])

```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/varsPercent_Placenta_male.png", width=1100, height=1400, res=300)
perc_vars_Placenta_male_ITU_n
dev.off()
```


```{r}
pm2_Placenta_male_ITU_n_coef <-
  dcast(pm2_Placenta_male_ITU_n[,
                       as.list(unlist(
                         lapply(.SD,
                                function(x) {
                                  y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                  list("non_zero" = 100 * mean(x != 0),
                                       lcl = y[1],
                                       ucl = y[2],
                                       width = diff(y),
                                       median = median(x[x!= 0]))
                                }))),
                       .SDcols = c("Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"),
                       by = nzero][order(nzero)] %>%
          melt(id.var = "nzero") %>%
          .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
          .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
          .[nzero ==nzero_final_placenta_male], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_Placenta_male_ITU_n_coef <-
  pm2_Placenta_male_ITU_n_coef[match(c("Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"), pm2_Placenta_male_ITU_n_coef$variable),]
pm2_Placenta_male_ITU_n_coef$variable <- factor(pm2_Placenta_male_ITU_n_coef$variabl, levels=unique(pm2_Placenta_male_ITU_n_coef$variable))


```

```{r}
sig_vars_Placenta_male_ITU_n <-
  pm2_Placenta_male_ITU_n_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x="nzero")+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf("%0.2f", round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients = 2", color="%")

```

```{r}
coef_Placenta_male_ITU_n <- 
  ggplot(pm2_Placenta_male_ITU_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Placenta_male_ITU_n
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/coef_Placenta_male.png", width=2800, height=1400, res=400)
coef_Placenta_male_ITU_n
dev.off()
```

```{r}
p1 <-
  csummary_Placenta_male_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
  ggplot(pm2_Placenta_male_ITU_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  ggtitle("nzero = 5")+
  theme(text = element_text(size =17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/Model_coef_Placenta_male.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()
```

[to the top](#top)

### females
  
```{r}
# in case you want to start from here
load("InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_n.Rdata")
Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_n$Child_Sex <- NULL
```


```{r}
yrc_mat_ITU_Placenta_female_n <- matrix(Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_n$EAAR_Lee)
xrc_mat_ITU_Placenta_female_n <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_Placenta_female_ITU_EAAR_noNa_n)[, -1]
yrc_mat_ITU_scaled_Placenta_female_n <- scale(yrc_mat_ITU_Placenta_female_n)
xrc_mat_ITU_scaled_Placenta_female_n <- scale(xrc_mat_ITU_Placenta_female_n)
```

<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Placenta_female_ITU_n <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_ITU_scaled_Placenta_female_n), replace = TRUE) -->
<!--     ensr(xrc_mat_ITU_scaled_Placenta_female_n[rws, ], yrc_mat_ITU_scaled_Placenta_female_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!--   end_time <- Sys.time() -->
<!--   end_time - start_time -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Placenta_female_ITU_n, file="InputData/Data_ElasticNets/bootstraps_Placenta_female_ITU_n_1000.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/bootstraps_Placenta_female_ITU_n_1000.Rdata")
```


```{r}
summaries_Placenta_female_ITU_n <-
  bootstraps_Placenta_female_ITU_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Placenta_female_ITU_n
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/bootstraps_Placenta_FEMALE.png", width=800, height=600)
summaries_Placenta_female_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```


<!-- ```{r, warning=FALSE} -->
<!-- # lowest cvm by bootstrap and nzero -->
<!-- pm_Placenta_female_ITU_n <- summaries_Placenta_female_ITU_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] -->
<!-- pm2_Placenta_female_ITU_n <- NULL -->

<!-- for(i in as.integer(seq(1, nrow(pm_Placenta_female_ITU_n), by = 1))) { -->
<!--   pm2_Placenta_female_ITU_n <- rbind(pm2_Placenta_female_ITU_n, -->
<!--                cbind(pm_Placenta_female_ITU_n[i, ], -->
<!--                t(as.matrix(coef(bootstraps_Placenta_female_ITU_n[[pm_Placenta_female_ITU_n[i, bootstrap]]][[pm_Placenta_female_ITU_n[i, l_index]]], s = pm_Placenta_female_ITU_n[i, lambda]))) -->
<!--                ) -->
<!--   ) -->
<!-- } -->

<!-- pm2_Placenta_female_ITU_n -->
<!-- ``` -->


<!-- ```{r} -->
<!-- # save "preferable models" -->
<!-- save(pm2_Placenta_female_ITU_n, file="InputData/Data_ElasticNets/pm2_Placenta_female_ITU_n.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/pm2_Placenta_female_ITU_n.Rdata")
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap
```


```{r}
csummary_Placenta_female_ITU_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                                       list(pm2_Placenta_female_ITU_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"), by = nzero]
                                            ,
                                            pm2_Placenta_female_ITU_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                            pm2_Placenta_female_ITU_n[, .(median_cvm = median(cvm)), by = nzero]
                                       ))[order(nzero)]

csummary_Placenta_female_ITU_n
```


```{r}
g1_Placenta_female_ITU_n <-
  csummary_Placenta_female_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
  

g2_Placenta_female_ITU_n <-
  csummary_Placenta_female_ITU_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Placenta_female_ITU_n, g2_Placenta_female_ITU_n, ncol = 1)

```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/Model_Placenta_female.png", width=2800, height=1400, res=400)
g1_Placenta_female_ITU_n
dev.off()
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/bootstrapModels_Placenta_female.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Placenta_female_ITU_n, g2_Placenta_female_ITU_n, ncol = 1)
dev.off()
```
```{r}
elbow_finder(csummary_Placenta_female_ITU_n$nzero, csummary_Placenta_female_ITU_n$median_cvm)

nzero_indices_Cord <- data.frame(t(elbow_finder(csummary_Placenta_female_ITU_n$nzero, csummary_Placenta_female_ITU_n$median_cvm)))
colnames(nzero_indices_Cord) <- c("x", "y")
rownames(nzero_indices_Cord) <- NULL
```

```{r}
nzero_final_placenta_female <- 7
```


```{r}
csummary_Placenta_female_ITU_n[nzero %in% nzero_final_placenta_female]
```


```{r}
nonzero_choose_Placenta_female <- ggplot2::ggplot(csummary_Placenta_female_ITU_n) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_Cord, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median of minimum cross-validation errors over bootstraps")+
  ggplot2::xlab("number of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[13], yend = median_cvm[13], colour = "segment"), data = csummary_Placenta_female_ITU_n, show.legend = F)

nonzero_choose_Placenta_female
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/nzero_choose_Placenta_female.png", width=1600, height=1400, res=300)
nonzero_choose_Placenta_female
dev.off()
```


```{r}
summary_Placenta_female_ITU_n_finalnzero <- csummary_Placenta_female_ITU_n[nzero %in% nzero_final_placenta_female]
sig_var_names_Placenta_female_ITU_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_Placenta_female_ITU_n_finalnzero[,!c("nzero", "mean_cvm", "median_cvm")]) %>% colnames()
colnames(summary_Placenta_female_ITU_n_finalnzero) <- c("non-zero", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "mean cvm", "median cvm")
summary_Placenta_female_ITU_n_finalnzeroT <- as.data.frame(t(summary_Placenta_female_ITU_n_finalnzero[,-c("non-zero", "median cvm", "mean cvm")]))
summary_Placenta_female_ITU_n_finalnzeroT$variable <- rownames(summary_Placenta_female_ITU_n_finalnzeroT)
rownames(summary_Placenta_female_ITU_n_finalnzeroT) <- NULL
names(summary_Placenta_female_ITU_n_finalnzeroT)[names(summary_Placenta_female_ITU_n_finalnzeroT) == 'V1'] <- 'percent'
summary_Placenta_female_ITU_n_finalnzeroT <- summary_Placenta_female_ITU_n_finalnzeroT[order(summary_Placenta_female_ITU_n_finalnzeroT$percent),]

summary_Placenta_female_ITU_n_finalnzeroT$number <- seq(1, length(summary_Placenta_female_ITU_n_finalnzeroT$variable))
```

```{r, fig.width=8}
perc_vars_Placenta_female_ITU_n <- 
  ggplot(summary_Placenta_female_ITU_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("% occurence in models with nzero coefficients = 7")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("variable")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()

perc_vars_Placenta_female_ITU_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Placenta_female_ITU_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])

```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/varsPercent_Placenta_female.png", width=1100, height=1400, res=300)
perc_vars_Placenta_female_ITU_n
dev.off()
```


  
```{r}
pm2_Placenta_female_ITU_n_coef <-
  dcast(pm2_Placenta_female_ITU_n[,
                                as.list(unlist(
                                  lapply(.SD,
                                         function(x) {
                                           y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                           list("non_zero" = 100 * mean(x != 0),
                                                lcl = y[1],
                                                ucl = y[2],
                                                width = diff(y),
                                                median = median(x[x!= 0]))
                                         }))),
                                .SDcols = c("Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"),
                                by = nzero][order(nzero)] %>%
          melt(id.var = "nzero") %>%
          .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
          .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
          .[nzero ==nzero_final_placenta_female], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_Placenta_female_ITU_n_coef <-
  pm2_Placenta_female_ITU_n_coef[match(c("Child_Birth_Weight", "Child_Birth_Length", "Child_Head_Circumference_At_Birth", "Delivery_mode_dichotomaided", "Induced_Labouryes", "Parity_dichotomgiven birth before", "Maternal_Age_Years", "Maternal_Body_Mass_Index_in_Early_Pregnancy", "Maternal_Hypertension_dichotomhypertension in current pregnancy", "Maternal_Diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_DisordersYes", "smoking_dichotomyes"), pm2_Placenta_female_ITU_n_coef$variable),]
pm2_Placenta_female_ITU_n_coef$variable <- factor(pm2_Placenta_female_ITU_n_coef$variabl, levels=unique(pm2_Placenta_female_ITU_n_coef$variable))

```


```{r}
sig_vars_Placenta_female_ITU_n <-
  pm2_Placenta_female_ITU_n_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x="nzero")+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf("%0.2f", round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients = 4", color="%")

```

```{r}
coef_Placenta_female_ITU_n <- 
  ggplot(pm2_Placenta_female_ITU_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Placenta_female_ITU_n
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/coef_Placenta_female.png",  width=2800, height=1400, res=400)
coef_Placenta_female_ITU_n
dev.off()
```

```{r}
p1 <-
  csummary_Placenta_female_ITU_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
coef_Placenta_female_ITU_n <- 
  ggplot(pm2_Placenta_female_ITU_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  ggtitle("nzero = 7")+
  theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/Model_coef_Placenta_female.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()
```

[to the top](#top)

**PREDO**

## Placenta elastic net {#elasticnetPlacentaPREDO}  


```{r}
# in case you want to start from here
load("InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n.Rdata")
```


```{r}
yrc_mat_PREDO_Placenta_n <- matrix(Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n$EAAR_Lee)
xrc_mat_PREDO_Placenta_n <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_Placenta_PREDO_EAAR_noNa_n)[, -1]
yrc_mat_PREDO_scaled_Placenta_n <- scale(yrc_mat_PREDO_Placenta_n)
xrc_mat_PREDO_scaled_Placenta_n <- scale(xrc_mat_PREDO_Placenta_n)
```

<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Placenta_PREDO_n <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_PREDO_scaled_Placenta_n), replace = TRUE) -->
<!--     ensr(xrc_mat_PREDO_scaled_Placenta_n[rws, ], yrc_mat_PREDO_scaled_Placenta_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!--   end_time <- Sys.time() -->
<!--   end_time - start_time -->

<!--   #Time difference of 3.159319 hours -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Placenta_PREDO_n, file="InputData/Data_ElasticNets/bootstraps_Placenta_PREDO_n_1000.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/bootstraps_Placenta_PREDO_n_1000.Rdata")
```


```{r}
summaries_Placenta_PREDO_n <-
  bootstraps_Placenta_PREDO_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Placenta_PREDO_n
```

```{r}
summaries_Placenta_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstraps_Placenta_PREDO.png", width=800, height=600)
summaries_Placenta_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```



<!-- ```{r, warning=FALSE} -->
<!-- # lowest cvm by bootstrap and nzero -->
<!-- pm_Placenta_PREDO_n <- summaries_Placenta_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] -->
<!-- pm2_Placenta_PREDO_n <- NULL -->

<!-- for(i in as.integer(seq(1, nrow(pm_Placenta_PREDO_n), by = 1))) { -->
<!--   pm2_Placenta_PREDO_n <- rbind(pm2_Placenta_PREDO_n, -->
<!--                cbind(pm_Placenta_PREDO_n[i, ], -->
<!--                t(as.matrix(coef(bootstraps_Placenta_PREDO_n[[pm_Placenta_PREDO_n[i, bootstrap]]][[pm_Placenta_PREDO_n[i, l_index]]], s = pm_Placenta_PREDO_n[i, lambda]))) -->
<!--                ) -->
<!--   ) -->
<!-- } -->

<!-- pm2_Placenta_PREDO_n -->
<!-- ``` -->


<!-- ```{r} -->
<!-- # save "preferable models" -->
<!-- save(pm2_Placenta_PREDO_n, file="InputData/Data_ElasticNets/pm2_Placenta_PREDO_n.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/pm2_Placenta_PREDO_n.Rdata")
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap
```


```{r}
csummary_Placenta_PREDO_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                                     list(pm2_Placenta_PREDO_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"), by = nzero]
                                          ,
                                          pm2_Placenta_PREDO_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                          pm2_Placenta_PREDO_n[, .(median_cvm = median(cvm)), by = nzero]
                                     ))[order(nzero)]

csummary_Placenta_PREDO_n
```


```{r}
g1_Placenta_PREDO_n <-
  csummary_Placenta_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex", "birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

g2_Placenta_PREDO_n <-
  csummary_Placenta_PREDO_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Placenta_PREDO_n, g2_Placenta_PREDO_n, ncol = 1)

```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstrapModels_Placenta_PREDO.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Placenta_PREDO_n, g2_Placenta_PREDO_n, ncol = 1)
dev.off()
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_Placenta_PREDO.png", width=2800, height=1400, res=400)
g1_Placenta_PREDO_n
dev.off()
```

```{r}
elbow_finder(csummary_Placenta_PREDO_n$nzero, csummary_Placenta_PREDO_n$median_cvm)

nzero_indices_Placenta_PREDO <- data.frame(t(elbow_finder(csummary_Placenta_PREDO_n$nzero, csummary_Placenta_PREDO_n$median_cvm)))
colnames(nzero_indices_Placenta_PREDO) <- c("x", "y")
rownames(nzero_indices_Placenta_PREDO) <- NULL
```
```{r}
nzero_final_placenta_predo <- 6
```


```{r}
csummary_Placenta_PREDO_n[nzero %in% nzero_final_placenta_predo]
```

```{r}
nonzero_choose_Placenta_PREDO <- ggplot2::ggplot(csummary_Placenta_PREDO_n) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_Placenta_PREDO, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median of minimum cross-validation errors over bootstraps")+
  ggplot2::xlab("number of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[14], yend = median_cvm[14], colour = "segment"), data = csummary_Placenta_PREDO_n, show.legend = F)

nonzero_choose_Placenta_PREDO
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/nzero_choose_Placenta_PREDO.png", width=1600, height=1400, res=300)
nonzero_choose_Placenta_PREDO
dev.off()
```

```{r}
summary_Placenta_PREDO_n_finalnzero <- csummary_Placenta_PREDO_n[nzero %in% nzero_final_placenta_predo]
sig_var_names_Placenta_PREDO_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_Placenta_PREDO_n_finalnzero[,!c("nzero", "mean_cvm", "median_cvm")]) %>% colnames()
colnames(summary_Placenta_PREDO_n_finalnzero) <- c("non-zero","child sex", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "mean cvm", "median cvm")
summary_Placenta_PREDO_n_finalnzeroT <- as.data.frame(t(summary_Placenta_PREDO_n_finalnzero[,-c("non-zero", "median cvm", "mean cvm")]))
summary_Placenta_PREDO_n_finalnzeroT$variable <- rownames(summary_Placenta_PREDO_n_finalnzeroT)
rownames(summary_Placenta_PREDO_n_finalnzeroT) <- NULL
names(summary_Placenta_PREDO_n_finalnzeroT)[names(summary_Placenta_PREDO_n_finalnzeroT) == 'V1'] <- 'percent'
summary_Placenta_PREDO_n_finalnzeroT <- summary_Placenta_PREDO_n_finalnzeroT[order(summary_Placenta_PREDO_n_finalnzeroT$percent),]

summary_Placenta_PREDO_n_finalnzeroT$number <- seq(1, length(summary_Placenta_PREDO_n_finalnzeroT$variable))
```

```{r, fig.width=8}
perc_vars_Placenta_PREDO_n <- 
  ggplot(summary_Placenta_PREDO_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("% occurence in models with nzero coefficients = 5")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("variable")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()

perc_vars_Placenta_PREDO_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Placenta_PREDO_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])

```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/varsPercent_Placenta_PREDO.png", width=1100, height=1400, res=400)
perc_vars_Placenta_PREDO_n
dev.off()
```


```{r}
pm2_Placenta_PREDO_n_coef <-
  dcast(pm2_Placenta_PREDO_n[,
                                as.list(unlist(
                                  lapply(.SD,
                                         function(x) {
                                           y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                           list("non_zero" = 100 * mean(x != 0),
                                                lcl = y[1],
                                                ucl = y[2],
                                                width = diff(y),
                                                median = median(x[x!= 0]))
                                         }))),
                                .SDcols = c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"),
                                by = nzero][order(nzero)] %>%
          melt(id.var = "nzero") %>%
          .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
          .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
          .[nzero ==nzero_final_placenta_predo], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_Placenta_PREDO_n_coef <-
  pm2_Placenta_PREDO_n_coef[match(c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"), pm2_Placenta_PREDO_n_coef$variable),]
pm2_Placenta_PREDO_n_coef$variable <- factor(pm2_Placenta_PREDO_n_coef$variabl, levels=unique(pm2_Placenta_PREDO_n_coef$variable))

## NOTE: median is used here instead of mean
# make frame for only significant variables:
pm2_Placenta_PREDO_n_datable <- dcast(pm2_Placenta_PREDO_n[,
                                                                 as.list(unlist(
                                                                   lapply(.SD,
                                                                          function(x) {
                                                                            y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                                                            list("non_zero" = 100 * mean(x != 0),
                                                                                 lcl = y[1],
                                                                                 ucl = y[2],
                                                                                 width = diff(y),
                                                                                 median = median(x[x!= 0]))
                                                                          }))),
                                                                 .SDcols = c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"),
                                                                 by = nzero][order(nzero)] %>%
                                           melt(id.var = "nzero") %>%
                                           .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
                                           .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
                                           # print %>%
                                           .[nzero == nzero_final_placenta_predo & variable %in% sig_var_names_Placenta_PREDO_n_finalnzero], nzero+ variable ~ metric, value.var="value")
pm2_Placenta_PREDO_n_coef
```


```{r}
write_xlsx(pm2_Placenta_PREDO_n_coef,"Results/Tables/CoefficientsModel_Placenta_PREDO.xlsx")
```

```{r}
sig_vars_Placenta_PREDO_n <-
  pm2_Placenta_PREDO_n_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x="nzero")+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf("%0.2f", round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients = 5", color="%")

```

```{r}
coef_Placenta_PREDO_n <- 
  ggplot(pm2_Placenta_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.5,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Placenta_PREDO_n
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/coef_Placenta_PREDO.png", width=2800, height=1400, res=400)
coef_Placenta_PREDO_n
dev.off()
```


```{r}
p1 <-
  csummary_Placenta_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
  ggplot(pm2_Placenta_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.5,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  ggtitle("nzero = 6")+
  theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_coef_Placenta_PREDO.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()
```

[to the top](#top)

## Placenta elastic net {#elasticnetPlacentaPREDO_a}  

 
```{r}
# in case you want to start from here
load("InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa.Rdata")
```

```{r}
yrc_mat_PREDO_Placenta_wa <- matrix(Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa$EAAR_Lee)
xrc_mat_PREDO_Placenta_wa <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_Placenta_PREDO_EAAR_noNa_wa)[, -1]
yrc_mat_PREDO_scaled_Placenta_wa <- scale(yrc_mat_PREDO_Placenta_wa)
xrc_mat_PREDO_scaled_Placenta_wa <- scale(xrc_mat_PREDO_Placenta_wa)
```

<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Placenta_PREDO_wa <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_PREDO_scaled_Placenta_wa), replace = TRUE) -->
<!--     ensr(xrc_mat_PREDO_scaled_Placenta_wa[rws, ], yrc_mat_PREDO_scaled_Placenta_wa[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!--   end_time <- Sys.time() -->
<!--   end_time - start_time -->

<!--   #Time difference of 3.159319 hours -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Placenta_PREDO_wa, file="InputData/Data_ElasticNets/bootstraps_Placenta_PREDO_wa_1000.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/bootstraps_Placenta_PREDO_wa_1000.Rdata")
```

```{r}
summaries_Placenta_PREDO_wa <-
  bootstraps_Placenta_PREDO_wa %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Placenta_PREDO_wa
```

```{r}
summaries_Placenta_PREDO_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/alcohol/bootstraps_Placenta_PREDO.png", width=800, height=600)
summaries_Placenta_PREDO_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```


<!-- ```{r, warning=FALSE} -->
<!-- # lowest cvm by bootstrap and nzero -->
<!-- pm_Placenta_PREDO_wa <- summaries_Placenta_PREDO_wa[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] -->
<!-- pm2_Placenta_PREDO_wa <- NULL -->

<!-- for(i in as.integer(seq(1, nrow(pm_Placenta_PREDO_wa), by = 1))) { -->
<!--   pm2_Placenta_PREDO_wa <- rbind(pm2_Placenta_PREDO_wa, -->
<!--                cbind(pm_Placenta_PREDO_wa[i, ], -->
<!--                t(as.matrix(coef(bootstraps_Placenta_PREDO_wa[[pm_Placenta_PREDO_wa[i, bootstrap]]][[pm_Placenta_PREDO_wa[i, l_index]]], s = pm_Placenta_PREDO_wa[i, lambda]))) -->
<!--                ) -->
<!--   ) -->
<!-- } -->

<!-- pm2_Placenta_PREDO_wa -->
<!-- ``` -->


<!-- ```{r} -->
<!-- # save "preferable models" -->
<!-- save(pm2_Placenta_PREDO_wa, file="InputData/Data_ElasticNets/pm2_Placenta_PREDO_wa.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/pm2_Placenta_PREDO_wa.Rdata")
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap
```

look how often a particular variable is associated with a non-zero coefficient in a model with a given number of non-zero coefficients (over all bootstraps)

```{r}
csummary_Placenta_PREDO_wa <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                                       list(pm2_Placenta_PREDO_wa[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes", "Alcohol_Use_In_Early_Pregnancy_19Octyes"), by = nzero]
                                            ,
                                            pm2_Placenta_PREDO_wa[, .(mean_cvm = mean(cvm)), by = nzero],
                                            pm2_Placenta_PREDO_wa[, .(median_cvm = median(cvm)), by = nzero]
                                       ))[order(nzero)]

csummary_Placenta_PREDO_wa
```


```{r}
g1_Placenta_PREDO_wa <-
  csummary_Placenta_PREDO_wa %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex","birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking", "maternal alcohol use"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

g2_Placenta_PREDO_wa <-
  csummary_Placenta_PREDO_wa %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Placenta_PREDO_wa, g2_Placenta_PREDO_wa, ncol = 1)

```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstrapModels_Placenta_PREDO.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Placenta_PREDO_wa, g2_Placenta_PREDO_wa, ncol = 1)
dev.off()
```
```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_Placena_PREDO.png", width=2800, height=1400, res=400)
g1_Placenta_PREDO_wa
dev.off()
```


```{r}
elbow_finder(csummary_Placenta_PREDO_wa$nzero, csummary_Placenta_PREDO_wa$median_cvm)

nzero_indices_Placenta_PREDO_wa<- data.frame(t(elbow_finder(csummary_Placenta_PREDO_wa$nzero, csummary_Placenta_PREDO_wa$median_cvm)))
colnames(nzero_indices_Placenta_PREDO_wa) <- c("x", "y")
rownames(nzero_indices_Placenta_PREDO_wa) <- NULL
```

look at models with 7 non-zero coefficient.

```{r}
nzero_final_placenta_predo_wa <- 9
```

```{r}
csummary_Placenta_PREDO_wa[nzero %in% nzero_final_placenta_predo_wa]
```

```{r}
nonzero_choose_Placenta_PREDO_wa <- ggplot2::ggplot(csummary_Placenta_PREDO_wa) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_Placenta_PREDO_wa, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median of minimum cross-validation errors over bootstraps")+
  ggplot2::xlab("number of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[14], yend = median_cvm[14], colour = "segment"), data = csummary_Placenta_PREDO_wa, show.legend = F)

nonzero_choose_Placenta_PREDO_wa
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/nzero_choose_Placenta_PREDO.png", width=1600, height=1400, res=300)
nonzero_choose_Placenta_PREDO_wa
dev.off()
```


```{r}
summary_Placenta_PREDO_wa_finalnzero <- csummary_Placenta_PREDO_wa[nzero %in% nzero_final_placenta_predo_wa]
sig_var_names_Placenta_PREDO_wa_finalnzero <- Filter(function(x) any(x > 0.75), summary_Placenta_PREDO_wa_finalnzero[,!c("nzero", "mean_cvm", "median_cvm")]) %>% colnames()
colnames(summary_Placenta_PREDO_wa_finalnzero) <- c("non-zero", "child sex", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal \alcohol use (yes)", "mean cvm", "median cvm")
summary_Placenta_PREDO_wa_finalnzeroT <- as.data.frame(t(summary_Placenta_PREDO_wa_finalnzero[,-c("non-zero", "median cvm", "mean cvm")]))
summary_Placenta_PREDO_wa_finalnzeroT$variable <- rownames(summary_Placenta_PREDO_wa_finalnzeroT)
rownames(summary_Placenta_PREDO_wa_finalnzeroT) <- NULL
names(summary_Placenta_PREDO_wa_finalnzeroT)[names(summary_Placenta_PREDO_wa_finalnzeroT) == 'V1'] <- 'percent'
summary_Placenta_PREDO_wa_finalnzeroT <- summary_Placenta_PREDO_wa_finalnzeroT[order(summary_Placenta_PREDO_wa_finalnzeroT$percent),]

summary_Placenta_PREDO_wa_finalnzeroT$number <- seq(1, length(summary_Placenta_PREDO_wa_finalnzeroT$variable))
```

```{r, fig.width=8}
perc_vars_Placenta_PREDO_wa <- 
  ggplot(summary_Placenta_PREDO_wa_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("% occurence in models with nzero coefficients = 8")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("variable")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()

perc_vars_Placenta_PREDO_wa

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Placenta_PREDO_wa_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])

```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/varsPercent_Placenta_PREDO.png", width=1100, height=1400, res=300)
perc_vars_Placenta_PREDO_wa
dev.off()
```

  
```{r}
pm2_Placenta_PREDO_wa_coef <-
  dcast(pm2_Placenta_PREDO_wa[,
                                as.list(unlist(
                                  lapply(.SD,
                                         function(x) {
                                           y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                           list("non_zero" = 100 * mean(x != 0),
                                                lcl = y[1],
                                                ucl = y[2],
                                                width = diff(y),
                                                median = median(x[x!= 0]))
                                         }))),
                                .SDcols = c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes","Alcohol_Use_In_Early_Pregnancy_19Octyes"),
                                by = nzero][order(nzero)] %>%
          melt(id.var = "nzero") %>%
          .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
          .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
          .[nzero == nzero_final_placenta_predo_wa], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_Placenta_PREDO_wa_coef <-
  pm2_Placenta_PREDO_wa_coef[match(c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes","Alcohol_Use_In_Early_Pregnancy_19Octyes"), pm2_Placenta_PREDO_wa_coef$variable),]
pm2_Placenta_PREDO_wa_coef$variable <- factor(pm2_Placenta_PREDO_wa_coef$variabl, levels=unique(pm2_Placenta_PREDO_wa_coef$variable))

## NOTE: median is used here instead of mean
# make frame for only significant variables:
pm2_Placenta_PREDO_wa_datable <- dcast(pm2_Placenta_PREDO_wa[,
                                                                 as.list(unlist(
                                                                   lapply(.SD,
                                                                          function(x) {
                                                                            y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                                                            list("non_zero" = 100 * mean(x != 0),
                                                                                 lcl = y[1],
                                                                                 ucl = y[2],
                                                                                 width = diff(y),
                                                                                 median = median(x[x!= 0]))
                                                                          }))),
                                                                 .SDcols = c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes","Alcohol_Use_In_Early_Pregnancy_19Octyes"),
                                                                 by = nzero][order(nzero)] %>%
                                           melt(id.var = "nzero") %>%
                                           .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
                                           .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
                                           # print %>%
                                           .[nzero == nzero_final_placenta_predo_wa& variable %in% sig_var_names_Placenta_PREDO_wa_finalnzero], nzero+ variable ~ metric, value.var="value")

```


```{r}
sig_vars_Placenta_PREDO_wa <-
  pm2_Placenta_PREDO_wa_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x="nzero")+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf("%0.2f", round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients = 9", color="%")


```

```{r}
coef_Placenta_PREDO_wa <- 
  ggplot(pm2_Placenta_PREDO_wa_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.5,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Placenta_PREDO_wa
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/coef_Placenta_PREDO.png", width=2800, height=1400, res=400)
coef_Placenta_PREDO_wa
dev.off()
```

```{r}
p1 <-
  csummary_Placenta_PREDO_wa %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
coef_Placenta_PREDO_wa <- 
  ggplot(pm2_Placenta_PREDO_wa_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.5,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "maternal alcohol use (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  ggtitle("nzero = 9")+
  theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_coef_Placenta_PREDO.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()
```

[to the top](#top)

## Placenta elastic net splitted by sex {#elasticnetPlacentaPREDO_s}  
model without alcohol variable, but splitted by sex

### males

```{r}
# in case you want to start from here
load("InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_n.Rdata")
Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_n$Child_Sex <- NULL
```


```{r}
yrc_mat_PREDO_Placenta_male_n <- matrix(Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_n$EAAR_Lee)
xrc_mat_PREDO_Placenta_male_n <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_Placenta_male_PREDO_EAAR_noNa_n)[, -1]
yrc_mat_PREDO_scaled_Placenta_male_n <- scale(yrc_mat_PREDO_Placenta_male_n)
xrc_mat_PREDO_scaled_Placenta_male_n <- scale(xrc_mat_PREDO_Placenta_male_n)
```

<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   bootstraps_Placenta_male_PREDO_n <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_PREDO_scaled_Placenta_male_n), replace = TRUE) -->
<!--     ensr(xrc_mat_PREDO_scaled_Placenta_male_n[rws, ], yrc_mat_PREDO_scaled_Placenta_male_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->
<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Placenta_male_PREDO_n, file="InputData/Data_ElasticNets/bootstraps_Placenta_male_PREDO_n_1000.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/bootstraps_Placenta_male_PREDO_n_1000.Rdata")
```

```{r}
summaries_Placenta_male_PREDO_n <-
  bootstraps_Placenta_male_PREDO_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Placenta_male_PREDO_n
```


```{r}
summaries_Placenta_male_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/bootstraps_Placenta_PREDO_MALE.png", width=800, height=600)
summaries_Placenta_male_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```


<!-- ```{r, warning=FALSE} -->
<!-- # lowest cvm by bootstrap and nzero -->
<!-- pm_Placenta_male_PREDO_n <- summaries_Placenta_male_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] -->
<!-- pm2_Placenta_male_PREDO_n <- NULL -->

<!-- for(i in as.integer(seq(1, nrow(pm_Placenta_male_PREDO_n), by = 1))) { -->
<!--   pm2_Placenta_male_PREDO_n <- rbind(pm2_Placenta_male_PREDO_n, -->
<!--                cbind(pm_Placenta_male_PREDO_n[i, ], -->
<!--                t(as.matrix(coef(bootstraps_Placenta_male_PREDO_n[[pm_Placenta_male_PREDO_n[i, bootstrap]]][[pm_Placenta_male_PREDO_n[i, l_index]]], s = pm_Placenta_male_PREDO_n[i, lambda]))) -->
<!--                ) -->
<!--   ) -->
<!-- } -->

<!-- pm2_Placenta_male_PREDO_n -->
<!-- ``` -->


<!-- ```{r} -->
<!-- # save "preferable models" -->
<!-- save(pm2_Placenta_male_PREDO_n, file="InputData/Data_ElasticNets/pm2_Placenta_male_PREDO_n.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/pm2_Placenta_male_PREDO_n.Rdata")
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap
```


```{r}
csummary_Placenta_male_PREDO_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                                       list(pm2_Placenta_male_PREDO_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"), by = nzero]
                                            ,
                                            pm2_Placenta_male_PREDO_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                            pm2_Placenta_male_PREDO_n[, .(median_cvm = median(cvm)), by = nzero]
                                       ))[order(nzero)]

csummary_Placenta_male_PREDO_n
```


```{r}
g1_Placenta_male_PREDO_n <-
  csummary_Placenta_male_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
  

g2_Placenta_male_PREDO_n <-
  csummary_Placenta_male_PREDO_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Placenta_male_PREDO_n, g2_Placenta_male_PREDO_n, ncol = 1)

```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/bootstrapModels_Placenta_PREDO_male.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Placenta_male_PREDO_n, g2_Placenta_male_PREDO_n, ncol = 1)
dev.off()
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/Model_Placenta_PREDO_male.png", width=2800, height=1400, res=400)
g1_Placenta_male_PREDO_n
dev.off()
```

```{r}
elbow_finder(csummary_Placenta_male_PREDO_n$nzero, csummary_Placenta_male_PREDO_n$median_cvm)

nzero_indices_Cord <- data.frame(t(elbow_finder(csummary_Placenta_male_PREDO_n$nzero, csummary_Placenta_male_PREDO_n$median_cvm)))
colnames(nzero_indices_Cord) <- c("x", "y")
rownames(nzero_indices_Cord) <- NULL
```
```{r}
nzero_final_placenta_male <- 5
```


```{r}
csummary_Placenta_male_PREDO_n[nzero %in% nzero_final_placenta_male]
```

```{r}
nonzero_choose_Placenta_male <- ggplot2::ggplot(csummary_Placenta_male_PREDO_n) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_Cord, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median of minimum cross-validation errors over bootstraps")+
  ggplot2::xlab("number of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[13], yend = median_cvm[13], colour = "segment"), data = csummary_Placenta_male_PREDO_n, show.legend = F)

nonzero_choose_Placenta_male
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/nzero_choose_Placenta_PREDO_male.png", width=1600, height=1400, res=300)
nonzero_choose_Placenta_male
dev.off()
```


```{r}
summary_Placenta_male_PREDO_n_finalnzero <- csummary_Placenta_male_PREDO_n[nzero %in% nzero_final_placenta_male]
sig_var_names_Placenta_male_PREDO_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_Placenta_male_PREDO_n_finalnzero[,!c("nzero", "mean_cvm", "median_cvm")]) %>% colnames()
colnames(summary_Placenta_male_PREDO_n_finalnzero) <- c("non-zero", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "mean cvm", "median cvm")
summary_Placenta_male_PREDO_n_finalnzeroT <- as.data.frame(t(summary_Placenta_male_PREDO_n_finalnzero[,-c("non-zero", "median cvm", "mean cvm")]))
summary_Placenta_male_PREDO_n_finalnzeroT$variable <- rownames(summary_Placenta_male_PREDO_n_finalnzeroT)
rownames(summary_Placenta_male_PREDO_n_finalnzeroT) <- NULL
names(summary_Placenta_male_PREDO_n_finalnzeroT)[names(summary_Placenta_male_PREDO_n_finalnzeroT) == 'V1'] <- 'percent'
summary_Placenta_male_PREDO_n_finalnzeroT <- summary_Placenta_male_PREDO_n_finalnzeroT[order(summary_Placenta_male_PREDO_n_finalnzeroT$percent),]

summary_Placenta_male_PREDO_n_finalnzeroT$number <- seq(1, length(summary_Placenta_male_PREDO_n_finalnzeroT$variable))
```

```{r, fig.width=8}
perc_vars_Placenta_male_PREDO_n <- 
  ggplot(summary_Placenta_male_PREDO_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("% occurence in models with nzero coefficients = 5")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("variable")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()

perc_vars_Placenta_male_PREDO_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Placenta_male_PREDO_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])

```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/varsPercent_Placenta_male.png", width=1100, height=1400, res=300)
perc_vars_Placenta_male_PREDO_n
dev.off()
```


  
```{r}
pm2_Placenta_male_PREDO_n_coef <-
  dcast(pm2_Placenta_male_PREDO_n[,
                                as.list(unlist(
                                  lapply(.SD,
                                         function(x) {
                                           y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                           list("non_zero" = 100 * mean(x != 0),
                                                lcl = y[1],
                                                ucl = y[2],
                                                width = diff(y),
                                                median = median(x[x!= 0]))
                                         }))),
                                .SDcols = c("Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"),
                                by = nzero][order(nzero)] %>%
          melt(id.var = "nzero") %>%
          .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
          .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
          .[nzero ==nzero_final_placenta_male], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_Placenta_male_PREDO_n_coef <-
  pm2_Placenta_male_PREDO_n_coef[match(c("Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"), pm2_Placenta_male_PREDO_n_coef$variable),]
pm2_Placenta_male_PREDO_n_coef$variable <- factor(pm2_Placenta_male_PREDO_n_coef$variabl, levels=unique(pm2_Placenta_male_PREDO_n_coef$variable))

## NOTE: median is used here instead of mean
# make frame for only significant variables:
pm2_Placenta_male_PREDO_n_datable <- dcast(pm2_Placenta_male_PREDO_n[,
                                                                 as.list(unlist(
                                                                   lapply(.SD,
                                                                          function(x) {
                                                                            y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                                                            list("non_zero" = 100 * mean(x != 0),
                                                                                 lcl = y[1],
                                                                                 ucl = y[2],
                                                                                 width = diff(y),
                                                                                 median = median(x[x!= 0]))
                                                                          }))),
                                                                 .SDcols = c("Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"),
                                                                 by = nzero][order(nzero)] %>%
                                           melt(id.var = "nzero") %>%
                                           .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
                                           .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
                                           # print %>%
                                           .[nzero == nzero_final_placenta_male & variable %in% sig_var_names_Placenta_male_PREDO_n_finalnzero], nzero+ variable ~ metric, value.var="value")

pm2_Placenta_male_PREDO_n_datable

```


```{r}
sig_vars_Placenta_male_PREDO_n <-
  pm2_Placenta_male_PREDO_n_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x="nzero")+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf("%0.2f", round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients = 5", color="%")
```

```{r}
coef_Placenta_male_PREDO_n <- 
  ggplot(pm2_Placenta_male_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Placenta_male_PREDO_n
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/coef_Placenta_PREDO_male.png", width=2800, height=1400, res=400)
coef_Placenta_male_PREDO_n
dev.off()
```


```{r}
p1 <-
  csummary_Placenta_male_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
coef_Placenta_male_PREDO_n <- 
  ggplot(pm2_Placenta_male_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  ggtitle("nzero = 5")+
  theme(text = element_text(size = 17), axis.title.x= element_text(size=13), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/Model_coef_Placenta_PREDO_male.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()
```

[to the top](#top)    
    
### females
```{r}
# in case you want to start from here
load("InputData/ClockCalculationsInput/Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_n.Rdata")
Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_n$Child_Sex <- NULL
```


```{r}
yrc_mat_PREDO_Placenta_female_n <- matrix(Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_n$EAAR_Lee)
xrc_mat_PREDO_Placenta_female_n <- model.matrix( ~ . - EAAR_Lee, data = Reg_Input_Data_Placenta_female_PREDO_EAAR_noNa_n)[, -1]
yrc_mat_PREDO_scaled_Placenta_female_n <- scale(yrc_mat_PREDO_Placenta_female_n)
xrc_mat_PREDO_scaled_Placenta_female_n <- scale(xrc_mat_PREDO_Placenta_female_n)
```

<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   bootstraps_Placenta_female_PREDO_n <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_PREDO_scaled_Placenta_female_n), replace = TRUE) -->
<!--     ensr(xrc_mat_PREDO_scaled_Placenta_female_n[rws, ], yrc_mat_PREDO_scaled_Placenta_female_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->
<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Placenta_female_PREDO_n, file="InputData/Data_ElasticNets/bootstraps_Placenta_female_PREDO_n_1000.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/bootstraps_Placenta_female_PREDO_n_1000.Rdata")
```


```{r}
summaries_Placenta_female_PREDO_n <-
  bootstraps_Placenta_female_PREDO_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Placenta_female_PREDO_n
```


```{r}
summaries_Placenta_female_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/bootstraps_Placenta_PREDO_female.png", width=800, height=600)
summaries_Placenta_female_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()
dev.off()
```


<!-- ```{r, warning=FALSE} -->
<!-- # lowest cvm by bootstrap and nzero -->
<!-- pm_Placenta_female_PREDO_n <- summaries_Placenta_female_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] -->
<!-- pm2_Placenta_female_PREDO_n <- NULL -->

<!-- for(i in as.integer(seq(1, nrow(pm_Placenta_female_PREDO_n), by = 1))) { -->
<!--   pm2_Placenta_female_PREDO_n <- rbind(pm2_Placenta_female_PREDO_n, -->
<!--                cbind(pm_Placenta_female_PREDO_n[i, ], -->
<!--                t(as.matrix(coef(bootstraps_Placenta_female_PREDO_n[[pm_Placenta_female_PREDO_n[i, bootstrap]]][[pm_Placenta_female_PREDO_n[i, l_index]]], s = pm_Placenta_female_PREDO_n[i, lambda]))) -->
<!--                ) -->
<!--   ) -->
<!-- } -->

<!-- pm2_Placenta_female_PREDO_n -->
<!-- ``` -->


<!-- ```{r} -->
<!-- # save "preferable models" -->
<!-- save(pm2_Placenta_female_PREDO_n, file="InputData/Data_ElasticNets/pm2_Placenta_female_PREDO_n.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/pm2_Placenta_female_PREDO_n.Rdata")
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap
```



```{r}
csummary_Placenta_female_PREDO_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                                         list(pm2_Placenta_female_PREDO_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"), by = nzero]
                                              ,
                                              pm2_Placenta_female_PREDO_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                              pm2_Placenta_female_PREDO_n[, .(median_cvm = median(cvm)), by = nzero]
                                         ))[order(nzero)]

csummary_Placenta_female_PREDO_n
```

```{r}
g1_Placenta_female_PREDO_n <-
  csummary_Placenta_female_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

g2_Placenta_female_PREDO_n <-
  csummary_Placenta_female_PREDO_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "number of non-zero coefficients")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Placenta_female_PREDO_n, g2_Placenta_female_PREDO_n, ncol = 1)

```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/bootstrapModels_Placenta_PREDO_female.png", width=2400, height=1800, res=300)
gridExtra::grid.arrange(g1_Placenta_female_PREDO_n, g2_Placenta_female_PREDO_n, ncol = 1)
dev.off()
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/Model_Placenta_PREDO_female.png", width=2800, height=1400, res=400)
g1_Placenta_female_PREDO_n
dev.off()
```


```{r}
elbow_finder(csummary_Placenta_female_PREDO_n$nzero, csummary_Placenta_female_PREDO_n$median_cvm)

nzero_indices_Cord <- data.frame(t(elbow_finder(csummary_Placenta_female_PREDO_n$nzero, csummary_Placenta_female_PREDO_n$median_cvm)))
colnames(nzero_indices_Cord) <- c("x", "y")
rownames(nzero_indices_Cord) <- NULL
```
```{r}
nzero_final_placenta_female <- 6
```


```{r}
csummary_Placenta_female_PREDO_n[nzero %in% nzero_final_placenta_female]
```

```{r}
nonzero_choose_Placenta_female <- ggplot2::ggplot(csummary_Placenta_female_PREDO_n) +
  ggplot2::theme_bw()+
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::scale_x_continuous(breaks=c(0:17))+
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::geom_point(data=nzero_indices_Cord, aes(x=x, y=y), colour="red", size=2)+
  ggplot2::ylab("median of minimum cross-validation errors over bootstraps")+
  ggplot2::xlab("number of non-zero coefficients")+
  ggplot2::geom_segment(aes(x = nzero[1], y = median_cvm[1], xend = nzero[13], yend = median_cvm[13], colour = "segment"), data = csummary_Placenta_female_PREDO_n, show.legend = F)

nonzero_choose_Placenta_female
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/nzero_choose_Placenta_PREDO_female.png", width=1600, height=1400, res=300)
nonzero_choose_Placenta_female
dev.off()
```



```{r}
summary_Placenta_female_PREDO_n_finalnzero <- csummary_Placenta_female_PREDO_n[nzero %in% nzero_final_placenta_female]
sig_var_names_Placenta_female_PREDO_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_Placenta_female_PREDO_n_finalnzero[,!c("nzero", "mean_cvm", "median_cvm")]) %>% colnames()
colnames(summary_Placenta_female_PREDO_n_finalnzero) <- c("non-zero", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "mean cvm", "median cvm")
summary_Placenta_female_PREDO_n_finalnzeroT <- as.data.frame(t(summary_Placenta_female_PREDO_n_finalnzero[,-c("non-zero", "median cvm", "mean cvm")]))
summary_Placenta_female_PREDO_n_finalnzeroT$variable <- rownames(summary_Placenta_female_PREDO_n_finalnzeroT)
rownames(summary_Placenta_female_PREDO_n_finalnzeroT) <- NULL
names(summary_Placenta_female_PREDO_n_finalnzeroT)[names(summary_Placenta_female_PREDO_n_finalnzeroT) == 'V1'] <- 'percent'
summary_Placenta_female_PREDO_n_finalnzeroT <- summary_Placenta_female_PREDO_n_finalnzeroT[order(summary_Placenta_female_PREDO_n_finalnzeroT$percent),]

summary_Placenta_female_PREDO_n_finalnzeroT$number <- seq(1, length(summary_Placenta_female_PREDO_n_finalnzeroT$variable))
```

```{r, fig.width=8}
perc_vars_Placenta_female_PREDO_n <- 
  ggplot(summary_Placenta_female_PREDO_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("% occurence in models with nzero coefficients = 4")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("variable")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()

perc_vars_Placenta_female_PREDO_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Placenta_female_PREDO_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])

```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/varsPercent_Placenta_female.png", width=1100, height=1400, res=300)
perc_vars_Placenta_female_PREDO_n
dev.off()
```


  
```{r}
pm2_Placenta_female_PREDO_n_coef <-
  dcast(pm2_Placenta_female_PREDO_n[,
                                  as.list(unlist(
                                    lapply(.SD,
                                           function(x) {
                                             y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                             list("non_zero" = 100 * mean(x != 0),
                                                  lcl = y[1],
                                                  ucl = y[2],
                                                  width = diff(y),
                                                  median = median(x[x!= 0]))
                                           }))),
                                  .SDcols = c("Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"),
                                  by = nzero][order(nzero)] %>%
          melt(id.var = "nzero") %>%
          .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
          .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
          .[nzero ==nzero_final_placenta_female], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_Placenta_female_PREDO_n_coef <-
  pm2_Placenta_female_PREDO_n_coef[match(c("Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"), pm2_Placenta_female_PREDO_n_coef$variable),]
pm2_Placenta_female_PREDO_n_coef$variable <- factor(pm2_Placenta_female_PREDO_n_coef$variabl, levels=unique(pm2_Placenta_female_PREDO_n_coef$variable))

## NOTE: median is used here instead of mean
# make frame for only significant variables:
pm2_Placenta_female_PREDO_n_datable <- dcast(pm2_Placenta_female_PREDO_n[,
                                                                     as.list(unlist(
                                                                       lapply(.SD,
                                                                              function(x) {
                                                                                y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                                                                list("non_zero" = 100 * mean(x != 0),
                                                                                     lcl = y[1],
                                                                                     ucl = y[2],
                                                                                     width = diff(y),
                                                                                     median = median(x[x!= 0]))
                                                                              }))),
                                                                     .SDcols = c("Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy","maternal_diabetes_dichotomdiabetes in current pregnancy","Maternal_Mental_Disorders_By_ChildbirthYes","smoking_dichotomyes"),
                                                                     by = nzero][order(nzero)] %>%
                                             melt(id.var = "nzero") %>%
                                             .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
                                             .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
                                             # print %>%
                                             .[nzero == nzero_final_placenta_female & variable %in% sig_var_names_Placenta_female_PREDO_n_finalnzero], nzero+ variable ~ metric, value.var="value")

pm2_Placenta_female_PREDO_n_datable
```


```{r}
sig_vars_Placenta_female_PREDO_n <-
  pm2_Placenta_female_PREDO_n_coef %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::theme(axis.text.x=element_blank())+
  ggplot2::aes(x="nzero")+
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero)) +
  ggplot2::geom_text(aes(y=variable, label=sprintf("%0.2f", round(median, digits=2)), size=50),hjust=0, vjust=0.5, nudge_x = 0.1)+
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::labs(y="predictor", x = "number of non-zero coefficients = 6", color="%")
```

```{r}
coef_Placenta_female_PREDO_n <- 
  ggplot(pm2_Placenta_female_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.5,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Placenta_female_PREDO_n
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/coef_Placenta_PREDO_female.png", width=2800, height=1400, res=400)
coef_Placenta_female_PREDO_n
dev.off()
```

```{r}
p1 <-
  csummary_Placenta_female_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
coef_Placenta_male_PREDO_n <- 
  ggplot(pm2_Placenta_female_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.5,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  ggtitle("nzero = 6")+
  theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_add/sex_split/Model_coef_Placenta_PREDO_female.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()
```

[to the top](#top)    

## Prediction in PREDO cord blood {#predictionCordPREDO}  
```{r}
ifelse(!dir.exists(file.path(getwd(), "Results/Figures/predPREDO")), dir.create(file.path(getwd(), "Results/Figures/predPREDO")), FALSE)
```

load PREDO data EPIC
```{r}
load("InputData/ClockCalculationsInput/Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n.Rdata")
```

load PREDO data 450K
```{r}
load("InputData/ClockCalculationsInput/Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n.Rdata")
```

load beta values from ITU
```{r}
load("InputData/Data_ElasticNets/Beta_Cord_ITU_n.Rdata")
Beta_Cord_ITU_n
```

prepare PREDO data EPIC
```{r}
y_mat_PREDO_Cord_pred <- matrix(Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n$EAAR_Bohlin)

Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_vars <- Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n[ ,c("Child_Sex", "Birth_Length", "Delivery_Mode_dichotom", "Maternal_Mental_Disorders_By_Childbirth", "smoking_dichotom")]

x_mat_PREDO_Cord_pred <- model.matrix(~ ., data= Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_vars)[, -1]

y_mat_PREDO_scaled_Cord_pred <- scale(y_mat_PREDO_Cord_pred)

x_mat_PREDO_scaled_Cord_pred <- scale(x_mat_PREDO_Cord_pred)
x_mat_PREDO_scaled_Cord_pred <- cbind(1, x_mat_PREDO_scaled_Cord_pred)
colnames(x_mat_PREDO_scaled_Cord_pred) <- c("Intercept", "child sex", "birth length", "delivery mode", "maternal mental disorders", "maternal smoking")
```

prepare PREDO data 450K
```{r}
y_mat_PREDO_Cord_predK <- matrix(Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n$EAAR_Bohlin)

Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_varsK <- Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n[ ,c("Child_Sex", "Birth_Length", "Delivery_Mode_dichotom",  "Maternal_Mental_Disorders_By_Childbirth", "smoking_dichotom")]

x_mat_PREDO_Cord_predK <- model.matrix(~ ., data= Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_varsK)[, -1]

y_mat_PREDO_scaled_Cord_predK <- scale(y_mat_PREDO_Cord_predK)

x_mat_PREDO_scaled_Cord_predK <- scale(x_mat_PREDO_Cord_predK)
x_mat_PREDO_scaled_Cord_predK <- cbind(1, x_mat_PREDO_scaled_Cord_predK)
colnames(x_mat_PREDO_scaled_Cord_predK) <- c("Intercept", "child sex", "birth length", "delivery mode","maternal mental disorders", "maternal smoking")
```

matrix multiplication EPIC
```{r}
#Y=X*B
y_pred_PREDO_cord <- x_mat_PREDO_scaled_Cord_pred %*% Beta_Cord_ITU_n
```

matrix multiplication 450K
```{r}
#Y=X*B
y_pred_PREDO_cordK <- x_mat_PREDO_scaled_Cord_predK %*% Beta_Cord_ITU_n
```

data EPIC
```{r}
PREDO_cord_pred_exp_real <- data.frame(cbind(y_pred_PREDO_cord, y_mat_PREDO_scaled_Cord_pred))
names(PREDO_cord_pred_exp_real) <- c("predicted_EAAR", "real_EAAR")
```

data 450K
```{r}
PREDO_cord_pred_exp_realK <- data.frame(cbind(y_pred_PREDO_cordK, y_mat_PREDO_scaled_Cord_predK))
names(PREDO_cord_pred_exp_realK) <- c("predicted_EAAR", "real_EAAR")
```

cor EPIC
```{r}
cor.test(PREDO_cord_pred_exp_real$predicted_EAAR,PREDO_cord_pred_exp_real$real_EAAR, alternative="greater")
# n = 144

plot_pred_real_epic <- ggscatter(PREDO_cord_pred_exp_real, x = "predicted_EAAR", y = "real_EAAR", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "predicted EAAR", ylab = "true EAAR", subtitle="PREDO EPIC (n=144)")+
   stat_cor(label.x = -0.4, label.y=3,p.accuracy = 0.001, r.accuracy = 0.01, alternative="greater")+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_text(size=12),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(-3,3), breaks = seq(-3,3, by=1))+
  scale_x_continuous(limits = c(-0.4,0.6), breaks = seq(-0.4,0.6, by=0.2))
```

r(142) = .24, p=0.002
n=144

cor 450K
```{r}
cor.test(PREDO_cord_pred_exp_realK$predicted_EAAR,PREDO_cord_pred_exp_realK$real_EAAR, alternative="greater")

plot_pred_real_450k <- ggscatter(PREDO_cord_pred_exp_realK, x = "predicted_EAAR", y = "real_EAAR", 
          add = "reg.line", conf.int = TRUE, 
          #cor.coef = TRUE, cor.method = "pearson",
          xlab = "predicted EAAR", ylab = "true EAAR", subtitle="PREDO 450K (n=766)")+
   stat_cor(label.x = -0.4, label.y=4, p.accuracy = 0.001, r.accuracy = 0.01, alternative="greater")+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_text(size=12),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(-4.5,4), breaks = seq(-4,4, by=1))+
  scale_x_continuous(limits = c(-0.5,0.8), breaks = seq(-0.4,0.8, by=0.2))
# n = 796
```

r(764) = .11, p=0.002
n=766

```{r}
ggarrange(plot_pred_real_epic, plot_pred_real_450k, nrow=1)
```

```{r}
png(file="Results/Figures/predPREDO/predictionEAARcord.png", width= 3600, height=2100, res=480)
ggarrange(plot_pred_real_epic, plot_pred_real_450k, nrow=1)
dev.off()

pdf(file="Results/Figures/predPREDO/predictionEAARcord.pdf", width= 10, height=5)
ggarrange(plot_pred_real_epic, plot_pred_real_450k, nrow=1)
dev.off()
```

## elastic net PREDO EPIC Cord blood {#elasticnetCordPREDO}  
main model, without alcohol

```{r}
load("InputData/ClockCalculationsInput/Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n.Rdata")
```


```{r}
yrc_mat_PREDO_Cord_n <- matrix(Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n$EAAR_Bohlin)
xrc_mat_PREDO_Cord_n <- model.matrix( ~ . - EAAR_Bohlin, data = Reg_Input_Data_Cordblood_PREDO_EAAR_noNa_n)[, -1]
yrc_mat_PREDO_scaled_Cord_n <- scale(yrc_mat_PREDO_Cord_n)
xrc_mat_PREDO_scaled_Cord_n <- scale(xrc_mat_PREDO_Cord_n)
```

<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Cord_PREDO_n <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_PREDO_scaled_Cord_n), replace = TRUE) -->
<!--     ensr(xrc_mat_PREDO_scaled_Cord_n[rws, ], yrc_mat_PREDO_scaled_Cord_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!--   end_time <- Sys.time() -->
<!--   end_time - start_time -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Cord_PREDO_n, file="InputData/Data_ElasticNets/bootstraps_Cord_PREDO_n_1000.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/bootstraps_Cord_PREDO_n_1000.Rdata")
```

first get a summary of all ensr objects
```{r}
summaries_Cord_PREDO_n <-
  bootstraps_Cord_PREDO_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Cord_PREDO_n
```


```{r}
summaries_Cord_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()+
  ggplot2::labs(x="\nnzero", y="cvm\n")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))+
  ggplot2::theme_bw()
  
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstraps_Cord_PREDO.png", width=2200, height=1400, res=300)
summaries_Cord_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()+
  ggplot2::labs(x="\nnzero", y="cvm\n")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))+
  ggplot2::theme_bw()
dev.off()
```


<!-- ```{r, warning=FALSE} -->
<!-- # lowest cvm by bootstrap and nzero -->
<!-- pm_Cord_PREDO_n <- summaries_Cord_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] -->
<!-- pm2_Cord_PREDO_n <- NULL -->

<!-- for(i in as.integer(seq(1, nrow(pm_Cord_PREDO_n), by = 1))) { -->
<!--   pm2_Cord_PREDO_n <- rbind(pm2_Cord_PREDO_n, -->
<!--                cbind(pm_Cord_PREDO_n[i, ], -->
<!--                t(as.matrix(coef(bootstraps_Cord_PREDO_n[[pm_Cord_PREDO_n[i, bootstrap]]][[pm_Cord_PREDO_n[i, l_index]]], s = pm_Cord_PREDO_n[i, lambda]))) -->
<!--                ) -->
<!--   ) -->
<!-- } -->

<!-- pm2_Cord_PREDO_n -->
<!-- ``` -->


<!-- ```{r} -->
<!-- # save "preferable models" -->
<!-- save(pm2_Cord_PREDO_n, file="InputData/Data_ElasticNets/pm2_Cord_PREDO_n.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/pm2_Cord_PREDO_n.Rdata")
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap
```

look how often a particular variable is associated with a non-zero coefficient in a model with a given number of non-zero coefficients (over all bootstraps)

```{r}
csummary_Cord_PREDO_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                               list(pm2_Cord_PREDO_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy", "maternal_diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_Disorders_By_ChildbirthYes", "smoking_dichotomyes"), by = nzero]
                                    ,
                                    pm2_Cord_PREDO_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                    pm2_Cord_PREDO_n[, .(median_cvm = median(cvm)), by = nzero]
                               ))[order(nzero)]

csummary_Cord_PREDO_n
```


```{r}
g1_Cord_PREDO_n <-
  csummary_Cord_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex", "birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor\n", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
  

g2_Cord_PREDO_n <-
  csummary_Cord_PREDO_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "nzero")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Cord_PREDO_n, g2_Cord_PREDO_n, ncol = 1)
g1_Cord_PREDO_n
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_Cord_PREDO.png", width=2800, height=1400, res=400)
g1_Cord_PREDO_n
dev.off()
```
```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstrapModels_Cord_PREDO.png", width=2800, height=1400, res=300)
gridExtra::grid.arrange(g1_Cord_PREDO_n, g2_Cord_PREDO_n, ncol = 1)
dev.off()
```


```{r}
elbow_finder(csummary_Cord_PREDO_n$nzero, csummary_Cord_PREDO_n$median_cvm)

nzero_indices_Cord <- data.frame(t(elbow_finder(csummary_Cord_PREDO_n$nzero, csummary_Cord_PREDO_n$median_cvm)))
colnames(nzero_indices_Cord) <- c("x", "y")
rownames(nzero_indices_Cord) <- NULL
```

```{r}
nzero_final_cord_predo <- 7
```

```{r}
csummary_Cord_PREDO_n[nzero %in% nzero_final_cord_predo]
```

```{r}
summary_Cord_PREDO_n_finalnzero <- csummary_Cord_PREDO_n[nzero %in% nzero_final_cord_predo]
sig_var_names_Cord_PREDO_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_Cord_PREDO_n_finalnzero[,!c("nzero", "mean_cvm", "median_cvm")]) %>% colnames()
colnames(summary_Cord_PREDO_n_finalnzero) <- c("non-zero", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "mean cvm", "median cvm")
summary_Cord_PREDO_n_finalnzeroT <- as.data.frame(t(summary_Cord_PREDO_n_finalnzero[,-c("non-zero", "median cvm", "mean cvm")]))
summary_Cord_PREDO_n_finalnzeroT$variable <- rownames(summary_Cord_PREDO_n_finalnzeroT)
rownames(summary_Cord_PREDO_n_finalnzeroT) <- NULL
names(summary_Cord_PREDO_n_finalnzeroT)[names(summary_Cord_PREDO_n_finalnzeroT) == 'V1'] <- 'percent'
summary_Cord_PREDO_n_finalnzeroT <- summary_Cord_PREDO_n_finalnzeroT[order(summary_Cord_PREDO_n_finalnzeroT$percent),]

summary_Cord_PREDO_n_finalnzeroT$number <- seq(1, length(summary_Cord_PREDO_n_finalnzeroT$variable))
```

```{r, fig.width=8}
perc_vars_Cord_PREDO_n <- 
  ggplot(summary_Cord_PREDO_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("\n% occurence in models with nzero coefficients = 9    ")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("predictor\n")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

perc_vars_Cord_PREDO_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Cord_PREDO_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])

```

```{r}
pm2_Cord_PREDO_n_coef <-
  dcast(pm2_Cord_PREDO_n[,
                                as.list(unlist(
                                  lapply(.SD,
                                         function(x) {
                                           y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                           list("non_zero" = 100 * mean(x != 0),
                                                lcl = y[1],
                                                ucl = y[2],
                                                width = diff(y),
                                                median = median(x[x!= 0]))
                                         }))),
                                .SDcols = c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy", "maternal_diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_Disorders_By_ChildbirthYes", "smoking_dichotomyes"),
                                by = nzero][order(nzero)] %>%
          melt(id.var = "nzero") %>%
          .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
          .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
          .[nzero ==nzero_final_cord_predo], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_Cord_PREDO_n_coef <-
  pm2_Cord_PREDO_n_coef[match(c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy", "maternal_diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_Disorders_By_ChildbirthYes", "smoking_dichotomyes"), pm2_Cord_PREDO_n_coef$variable),]
pm2_Cord_PREDO_n_coef$variable <- factor(pm2_Cord_PREDO_n_coef$variabl, levels=unique(pm2_Cord_PREDO_n_coef$variable))

```

```{r}
write_xlsx(pm2_Cord_PREDO_n_coef,"Results/Tables/Coefficients_Cord_PREDO.xlsx")
```

```{r}
coef_Cord_PREDO_n <- 
  ggplot(pm2_Cord_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Cord_PREDO_n
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/coef_Cord_PREDO.png",  width=2800, height=1400, res=400)
coef_Cord_PREDO_n
dev.off()
```


```{r}
p1 <-
  csummary_Cord_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor\n", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
  ggplot(pm2_Cord_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
   ggtitle("nzero = 7")+
  theme_bw()+
 theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_coef_Cord_PREDO.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()
```

[to the top](#top)

## elastic net PREDO 450K Cord blood {#elasticnetCordPREDO450}  
main model, without alcohol

```{r}
load("InputData/ClockCalculationsInput/Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n.Rdata")
```


```{r}
yrc_mat_PREDO_Cord450_n <- matrix(Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n$EAAR_Bohlin)
xrc_mat_PREDO_Cord450_n <- model.matrix( ~ . - EAAR_Bohlin, data = Reg_Input_Data_Cordblood_PREDO450K_EAAR_noNa_n)[, -1]
yrc_mat_PREDO_scaled_Cord450_n <- scale(yrc_mat_PREDO_Cord450_n)
xrc_mat_PREDO_scaled_Cord450_n <- scale(xrc_mat_PREDO_Cord450_n)
```


<!-- set seed -->
<!-- ```{r} -->
<!-- set.seed(2020) -->
<!-- ``` -->


<!-- ```{r, warning=F} -->
<!--   nboot = 1000 -->

<!--   start_time <- Sys.time() -->
<!--   bootstraps_Cord450_PREDO_n <- replicate(nboot, { -->
<!--     rws <- sample(1:nrow(xrc_mat_PREDO_scaled_Cord450_n), replace = TRUE) -->
<!--     ensr(xrc_mat_PREDO_scaled_Cord450_n[rws, ], yrc_mat_PREDO_scaled_Cord450_n[rws, ], standardized = FALSE, family="gaussian", nlambda=100, nfolds=10, alpha=c(0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)) -->
<!--   }, -->
<!--   simplify = FALSE) -->

<!--   end_time <- Sys.time() -->
<!--   end_time - start_time -->

<!-- ``` -->

<!-- ```{r} -->
<!-- save(bootstraps_Cord450_PREDO_n, file="InputData/Data_ElasticNets/bootstraps_Cord450_PREDO_n_1000.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/bootstraps_Cord450_PREDO_n_1000.Rdata")
```

```{r}
summaries_Cord450_PREDO_n <-
  bootstraps_Cord450_PREDO_n %>%
  lapply(summary) %>%
  rbindlist(idcol = "bootstrap")

summaries_Cord450_PREDO_n
```

```{r}
summaries_Cord450_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()+
  ggplot2::labs(x="\nnzero", y="cvm\n")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))+
  ggplot2::theme_bw()
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstraps_Cord450.png", width=2200, height=1400, res=300)
summaries_Cord450_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] %>%
  ggplot2::ggplot(data = .) +
  ggplot2::aes(x = nzero, y = cvm, group = bootstrap) +
  ggplot2::geom_point() +
  ggplot2::geom_line()+
  ggplot2::labs(x="\nnzero", y="cvm\n")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))+
  ggplot2::theme_bw()
dev.off()
```



<!-- ```{r, warning=FALSE} -->
<!-- # lowest cvm by bootstrap and nzero -->
<!-- pm_Cord450_PREDO_n <- summaries_Cord450_PREDO_n[, .SD[cvm == min(cvm)], by = c("bootstrap", "nzero")] -->
<!-- pm2_Cord450_PREDO_n <- NULL -->

<!-- for(i in as.integer(seq(1, nrow(pm_Cord450_PREDO_n), by = 1))) { -->
<!--   pm2_Cord450_PREDO_n <- rbind(pm2_Cord450_PREDO_n, -->
<!--                cbind(pm_Cord450_PREDO_n[i, ], -->
<!--                t(as.matrix(coef(bootstraps_Cord450_PREDO_n[[pm_Cord450_PREDO_n[i, bootstrap]]][[pm_Cord450_PREDO_n[i, l_index]]], s = pm_Cord450_PREDO_n[i, lambda]))) -->
<!--                ) -->
<!--   ) -->
<!-- } -->

<!-- pm2_Cord450_PREDO_n -->
<!-- ``` -->


<!-- ```{r} -->
<!-- # save "preferable models" -->
<!-- save(pm2_Cord450_PREDO_n, file="InputData/Data_ElasticNets/pm2_Cord450_PREDO_n.Rdata") -->
<!-- ``` -->


```{r}
load("InputData/Data_ElasticNets/pm2_Cord450_PREDO_n.Rdata")
# coefficient values for the models with smallest cvm by number of non-erzo coefficients and bootstrap
```

```{r}
csummary_Cord450_PREDO_n <- Reduce(function(x,y) merge(x = x, y = y, by = "nzero"), 
                               list(pm2_Cord450_PREDO_n[, lapply(.SD, function(x) {mean(x != 0)}), .SDcols = c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy", "maternal_diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_Disorders_By_ChildbirthYes", "smoking_dichotomyes"), by = nzero]
                                    ,
                                    pm2_Cord450_PREDO_n[, .(mean_cvm = mean(cvm)), by = nzero],
                                    pm2_Cord450_PREDO_n[, .(median_cvm = median(cvm)), by = nzero]
                               ))[order(nzero)]

csummary_Cord450_PREDO_n
```


```{r}
g1_Cord450_PREDO_n <-
  csummary_Cord450_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex", "birth weight", "birth length", "head circumference", "delivery mode", "induced labor", "parity", "maternal age", "maternal BMI", "maternal hypertension", "maternal diabetes", "maternal mental disorders", "maternal smoking"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor\n", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))
  

g2_Cord450_PREDO_n <-
  csummary_Cord450_PREDO_n %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero, y = median_cvm) +
  ggplot2::geom_point() + ggplot2::geom_line()+
  ggplot2::labs(y="median cvm", x = "nzero")+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::theme(axis.text=element_text(size=15),axis.title=element_text(size=18))

gridExtra::grid.arrange(g1_Cord450_PREDO_n, g2_Cord450_PREDO_n, ncol = 1)
g1_Cord450_PREDO_n
```


```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_Cord450_PREDO.png", width=2800, height=1400, res=400)
g1_Cord450_PREDO_n
dev.off()
```
```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/bootstrapModels_Cord450_PREDO.png", width=2800, height=1400, res=300)
gridExtra::grid.arrange(g1_Cord450_PREDO_n, g2_Cord450_PREDO_n, ncol = 1)
dev.off()
```

```{r}
elbow_finder(csummary_Cord450_PREDO_n$nzero, csummary_Cord450_PREDO_n$median_cvm)

nzero_indices_Cord450 <- data.frame(t(elbow_finder(csummary_Cord450_PREDO_n$nzero, csummary_Cord450_PREDO_n$median_cvm)))
colnames(nzero_indices_Cord450) <- c("x", "y")
rownames(nzero_indices_Cord450) <- NULL
```

```{r}
nzero_final_Cord450_predo <- 6
```

```{r}
csummary_Cord450_PREDO_n[nzero %in% nzero_final_Cord450_predo]
```

```{r}
summary_Cord450_PREDO_n_finalnzero <- csummary_Cord450_PREDO_n[nzero %in% nzero_final_Cord450_predo]
sig_var_names_Cord450_PREDO_n_finalnzero <- Filter(function(x) any(x > 0.75), summary_Cord450_PREDO_n_finalnzero[,!c("nzero", "mean_cvm", "median_cvm")]) %>% colnames()
colnames(summary_Cord450_PREDO_n_finalnzero) <- c("non-zero", "child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)", "mean cvm", "median cvm")
summary_Cord450_PREDO_n_finalnzeroT <- as.data.frame(t(summary_Cord450_PREDO_n_finalnzero[,-c("non-zero", "median cvm", "mean cvm")]))
summary_Cord450_PREDO_n_finalnzeroT$variable <- rownames(summary_Cord450_PREDO_n_finalnzeroT)
rownames(summary_Cord450_PREDO_n_finalnzeroT) <- NULL
names(summary_Cord450_PREDO_n_finalnzeroT)[names(summary_Cord450_PREDO_n_finalnzeroT) == 'V1'] <- 'percent'
summary_Cord450_PREDO_n_finalnzeroT <- summary_Cord450_PREDO_n_finalnzeroT[order(summary_Cord450_PREDO_n_finalnzeroT$percent),]

summary_Cord450_PREDO_n_finalnzeroT$number <- seq(1, length(summary_Cord450_PREDO_n_finalnzeroT$variable))
```

```{r, fig.width=8}
perc_vars_Cord450_PREDO_n <- 
  ggplot(summary_Cord450_PREDO_n_finalnzeroT, aes(reorder(variable, percent), percent, group=1))+
  geom_point()+ geom_line()+
  ylab("\n% occurence in models with nzero coefficients = 9    ")+
  scale_y_continuous(breaks=c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))+
  xlab("predictor\n")+
  coord_flip()+
  geom_hline(yintercept=0.75, linetype="dotted")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

perc_vars_Cord450_PREDO_n

# decide for cut-off % -> here .75

Filter(function(x) any(x > 0.75), summary_Cord450_PREDO_n_finalnzero[,!c("non-zero", "mean cvm", "median cvm")])

```


```{r}
pm2_Cord450_PREDO_n_coef <-
  dcast(pm2_Cord450_PREDO_n[,
                                as.list(unlist(
                                  lapply(.SD,
                                         function(x) {
                                           y <- unname(quantile(x[x != 0], probs = c(0.025, 0.975)))
                                           list("non_zero" = 100 * mean(x != 0),
                                                lcl = y[1],
                                                ucl = y[2],
                                                width = diff(y),
                                                median = median(x[x!= 0]))
                                         }))),
                                .SDcols = c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy", "maternal_diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_Disorders_By_ChildbirthYes", "smoking_dichotomyes"),
                                by = nzero][order(nzero)] %>%
          melt(id.var = "nzero") %>%
          .[, metric := sub("^.+\\.(.+)$", "\\1", variable)] %>%
          .[, variable := sub("^(.+)\\..+$", "\\1", variable)] %>%
          .[nzero ==nzero_final_Cord450_predo], nzero+ variable ~ metric, value.var="value")

# get desired order of predictors
pm2_Cord450_PREDO_n_coef <-
  pm2_Cord450_PREDO_n_coef[match(c("Child_Sexfemale", "Birth_Weight", "Birth_Length", "Head_Circumference_at_Birth", "Delivery_Mode_dichotomaided", "inducedlabourYes", "Parity_dichotomgiven birth before", "Maternal_Age_18PopRegandBR", "Maternal_PrepregnancyBMI18oct28new", "maternal_hypertension_dichotomhypertension in current pregnancy", "maternal_diabetes_dichotomdiabetes in current pregnancy", "Maternal_Mental_Disorders_By_ChildbirthYes", "smoking_dichotomyes"), pm2_Cord450_PREDO_n_coef$variable),]
pm2_Cord450_PREDO_n_coef$variable <- factor(pm2_Cord450_PREDO_n_coef$variabl, levels=unique(pm2_Cord450_PREDO_n_coef$variable))

```

```{r}
write_xlsx(pm2_Cord450_PREDO_n_coef,"Results/Tables/Coefficients_Cord450_PREDO.xlsx")
```

```{r}
coef_Cord450_PREDO_n <- 
  ggplot(pm2_Cord450_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="predictor", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


coef_Cord450_PREDO_n
```

```{r}
png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/coef_Cord450_PREDO.png",  width=2800, height=1400, res=400)
coef_Cord450_PREDO_n
dev.off()
```


```{r}
p1 <-
  csummary_Cord450_PREDO_n %>%
  melt(id.vars = c("nzero", "mean_cvm", "median_cvm")) %>%
  ggplot2::ggplot(.) +
  ggplot2::theme_bw() +
  ggplot2::aes(x = nzero) +
  ggplot2::geom_point(mapping = ggplot2::aes(y = variable, size = value, alpha = value, color = value*100)) +
  ggplot2::scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50)+
  ggplot2::scale_alpha(guide = 'none')+
  ggplot2::scale_size(guide = 'none')+
  ggplot2::scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  ggplot2::scale_x_continuous(breaks=0:14, labels=)+
  ggplot2::labs(y="predictor\n", x = "\nnumber of non-zero coefficients", color="%")+
  ggplot2::theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), legend.position = "none")
  
p2 <- 
  ggplot(pm2_Cord450_PREDO_n_coef, aes(y = variable, x=median))+
  geom_point(mapping = ggplot2::aes(y = variable, size =non_zero, alpha = non_zero, color = non_zero))+
  scale_color_gradient2(high = 'green', mid = "purple", low = "black", midpoint =50, limits=c(0,100))+
  scale_alpha(guide = 'none')+
  scale_size(guide = 'none')+
  geom_point()+
  geom_errorbar(aes(y = variable, xmin = lcl, xmax = ucl), width = 0.2)+
  labs(y="", x = "\nmedian & 95% CI of coefficient (over bootstraps)", color="%")+
  scale_x_continuous(limits=c(-0.4,0.4), breaks=c(-.4,-.3,-.2, -.1, 0, .1, .2, .3, .4))+
  scale_y_discrete(labels= c("child sex (female)", "birth weight", "birth length", "head circumference", "delivery mode (aided)", "induced labor (yes)", "parity (birth before)", "maternal age", "maternal BMI", "maternal hypertension (yes)", "maternal diabetes (yes)", "maternal mental disorders (yes)", "maternal smoking (yes)"))+
  geom_vline(xintercept=0, linetype="dashed")+
   ggtitle("nzero = 6")+
  theme_bw()+
 theme(text = element_text(size = 17), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15), plot.title = element_text(size=15), axis.text.y=element_blank())

g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g <- cbind(g1, g2, size = "last")
g$heights <- unit.pmax(g1$heights, g2$heights)

png(filename="Results/Figures/elasticNet_singleTissues/Outcome_main/Model_coef_Cord450_PREDO.png", width=5800, height=1600, res=400)
grid.draw(g)
dev.off()
```

```{r}
rm(list = setdiff(ls(), lsf.str()))
```

[to the top](#top) 

# Cross-Tissues

## Correlations DNAmGA {#corTissuesDNAmGA}  
```{r}
load(file= "InputData/ClockCalculationsInput/Data_CVS_ITU.Rdata")
load(file= "InputData/ClockCalculationsInput/Data_Cord_ITU.Rdata")
load(file= "InputData/ClockCalculationsInput/Data_Placenta_ITU.Rdata")
load(file="InputData/ClockCalculationsInput/Data_Full_ITU.Rdata") # data persons with all measurement points available
load(file="InputData/ClockCalculationsInput/Data_Cord_Placenta_ITU.Rdata")
load(file="InputData/ClockCalculationsInput/Data_CVS_Placenta_ITU.Rdata")
load(file="InputData/ClockCalculationsInput/Data_CVS_Cord_ITU.Rdata")
load(file="InputData/ClockCalculationsInput/Data_ITU_all.Rdata") # all persons together in one data frame

load(file= "InputData/ClockCalculationsInput/Data_Placenta_male_ITU.Rdata")
load(file= "InputData/ClockCalculationsInput/Data_Placenta_female_ITU.Rdata")

load(file="InputData/ClockCalculationsInput/Data_PREDO_450Kcord.Rdata")
load(file="InputData/ClockCalculationsInput/Data_PREDO_EPICcord.Rdata")
load(file="InputData/ClockCalculationsInput/Data_PREDO_EPICplacenta.Rdata")
load(file="InputData/ClockCalculationsInput/Data_PREDO_EPIC_Cord_Placenta.Rdata")
load(file="InputData/ClockCalculationsInput/Data_PREDO_EPIC_all.Rdata") # all persons with EPIC data together in one data frame

load(file="InputData/ClockCalculationsInput/Data_PREDO_Placenta_male.Rdata")
load(file="InputData/ClockCalculationsInput/Data_PREDO_Placenta_female.Rdata")
```

*Cord blood & Placenta (in ITU)*
```{r}
DNAmGAs_birth <- Data_Cord_Placenta_ITU[ ,c("DNAmGA_Bohlin","DNAmGA_Lee", "Gestational_Age_Weeks")]
colnames(DNAmGAs_birth) <- c("Cordblood", "Placenta", "GA_birth")
```

```{r}
BirthcorrDNAmGAs <- rcorr(as.matrix(DNAmGAs_birth))
BirthcorrDNAmGAs
```

adjusting for GA at birth
```{r}
# partial correlation
pcor.test(x=DNAmGAs_birth$Cordblood, y=DNAmGAs_birth$Placenta, z=DNAmGAs_birth$GA_birth)
```

```{r}
cor_cord_placenta_dnamga <-ggscatter(Data_Cord_Placenta_ITU, x = "DNAmGA_Bohlin", y = "DNAmGA_Lee", 
          add = "reg.line", conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = "pearson",
          xlab = "DNAm GA cord blood (weeks)", ylab = "DNAmGA Placenta (weeks)", subtitle=" ITU (n = 390)")+
   stat_cor(label.x = 32, label.y=44,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_text(size=12),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank())+
  scale_y_continuous(limits = c(30,44), breaks = seq(30,44, by=2))+
 scale_x_continuous(limits = c(32,44), breaks = seq(32,44, by=2))
```

```{r}
png(file="Results/Figures/diffTissues/DNAmGA_Cord_Placenta_ITU.png", width= 2600, height=1600, res=500)
cor_cord_placenta_dnamga
dev.off()
```


*Cord blood and Placenta (in PREDO)*
```{r}
DNAmGAsPREDO <- Data_PREDO_EPIC_Cord_Placenta[ ,c("DNAmGA_Bohlin","DNAmGA_Lee", "Gestational_Age")]
colnames(DNAmGAsPREDO) <- c("Cordblood", "Placenta", "GA_birth")
```

```{r}
allcorrsDNAmGAsPREDO <- rcorr(as.matrix(DNAmGAsPREDO))
allcorrsDNAmGAsPREDO
```

```{r}
# partial correlation
pcor.test(x=DNAmGAsPREDO$Cord, y=DNAmGAsPREDO$Placenta, z=DNAmGAsPREDO[,c("GA_birth")])
```


```{r}
cor_cord_placenta_dnamga_predo <-ggscatter(Data_PREDO_EPIC_Cord_Placenta, x = "DNAmGA_Bohlin", y = "DNAmGA_Lee", 
          add = "reg.line", conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = "pearson",
          xlab = "DNAm GA cord blood (weeks)", ylab = "DNAmGA Placenta (weeks)", subtitle=" PREDO (n = 116)")+
   stat_cor(label.x = 34, label.y=42,p.accuracy = 0.001, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_text(size=12),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank()) +
  scale_y_continuous(limits = c(32,42), breaks = seq(32,42, by=2))+
 scale_x_continuous(limits = c(34,42), breaks = seq(34,42, by=2))
```

```{r}
png(file="Results/Figures/diffTissues/DNAmGA_Cord_Placenta_PREDO.png", width= 2600, height=1600, res=500)
cor_cord_placenta_dnamga_predo
dev.off()
```


*CVS and Placenta*

```{r}
DNAmGAs_CP <- Data_CVS_Placenta_ITU[ ,c("DNAmGA_Lee_CVS","DNAmGA_Lee_Placenta", "gestage_at_CVS_weeks", "Gestational_Age_Weeks")]
colnames(DNAmGAs_CP) <- c("CVS", "Placenta", "GA_CVS", "GA_Birth")
```

```{r}
CPcorrDNAmGAs <- rcorr(as.matrix(DNAmGAs_CP))
CPcorrDNAmGAs
```

```{r}
# partial correlation
pcor.test(x=DNAmGAs_CP$CVS, y=DNAmGAs_CP$Placenta, z=DNAmGAs_CP[,c("GA_CVS","GA_Birth")])
```

```{r}
cor_cvs_placenta_dnamga <-ggscatter(Data_CVS_Placenta_ITU, x = "DNAmGA_Lee_CVS", y = "DNAmGA_Lee_Placenta", 
          add = "reg.line", conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = "pearson",
          xlab = "DNAm GA CVS (weeks)", ylab = "DNAmGA placenta (weeks)", subtitle=" ITU (n = 86)")+
   stat_cor(label.x = 6, label.y=44, p.accuracy = 0.01, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_text(size=12),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank())+
  scale_y_continuous(limits = c(34,44), breaks = seq(34,44, by=2))+
 scale_x_continuous(limits = c(6,14), breaks = seq(6,14, by=2))
```

```{r}
png(file="Results/Figures/diffTissues/DNAmGA_CVS_Placenta.png", width= 2600, height=1600, res=500)
cor_cvs_placenta_dnamga
dev.off()
```


*CVS and Cord blood*
```{r}
DNAmGAs_CC <- Data_CVS_Cord_ITU[ ,c("DNAmGA_Lee","DNAmGA_Bohlin", "gestage_at_CVS_weeks", "Gestational_Age_Weeks")]
colnames(DNAmGAs_CC) <- c("CVS", "Cord blood", "GA_CVS", "GA_Birth")
```

```{r}
CCcorrDNAmGAs <- rcorr(as.matrix(DNAmGAs_CC))
CCcorrDNAmGAs
```

```{r}
# partial correlation
pcor.test(x=DNAmGAs_CC$CVS, y=DNAmGAs_CC$Cord, z=DNAmGAs_CC[,c("GA_CVS","GA_Birth")])
```

```{r}
cor_cvs_cord_dnamga <- ggscatter(Data_CVS_Cord_ITU, x = "DNAmGA_Lee", y = "DNAmGA_Bohlin", 
          add = "reg.line", conf.int = TRUE, 
         # cor.coef = TRUE, cor.method = "pearson",
          xlab = "DNAm GA CVS (weeks)", ylab = "DNAmGA cord blood (weeks)", subtitle=" ITU (n = 73)")+
   stat_cor(label.x = 6, label.y=42,p.accuracy = 0.01, r.accuracy = 0.01)+
  theme(axis.text.x = element_text(size=9), axis.text.y=element_text(size=9), axis.title.y = element_text(size=12), axis.title.x=element_text(size=12),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank())+
  scale_y_continuous(limits = c(32,42), breaks = seq(32,42, by=2))+
 scale_x_continuous(limits = c(6,14), breaks = seq(6,14, by=2))
```

```{r}
png(file="Results/Figures/diffTissues/DNAmGA_CVS_Cord.png", width= 2600, height=1600, res=500)
cor_cvs_cord_dnamga
dev.off()
```


[to the top](#top)  

## Correspondence EAAR {#corTissuesEAAR}  

Fig. 4
*Cord blood & Placenta (in ITU)*
```{r}
DNAmGAResidsCBirth <- Data_Cord_Placenta_ITU[ ,c("EAAR_Bohlin","EAAR_Lee")]
colnames(DNAmGAResidsCBirth) <- c("Cordblood", "Placenta")
```

```{r}
allcorrsDNAmGAResidCBirth <- rcorr(as.matrix(DNAmGAResidsCBirth))
allcorrsDNAmGAResidCBirth
```


```{r}
cor_cord_placenta_resid <- ggscatter(Data_Cord_Placenta_ITU, x = "EAAR_Bohlin", y = "EAAR_Lee", 
          add = "reg.line", conf.int = TRUE, 
          xlab = "EAAR Cord blood", ylab = "EAAR fetal Placenta")+
          stat_cor(method = "pearson", label.x = -2, label.y = 4, r.digits = 1, p.digits = 2)+
          geom_hline(yintercept=0, linetype="dashed")+
          geom_vline(xintercept=0, linetype="dashed")+
  theme(text = element_text(size=13), axis.text.x = element_text(size=13))+
          coord_cartesian(xlim = c(-2, 2), ylim=c(-4,4)) 

cor_cord_placenta_resid

cor_cord_placenta_resid_f <- ggscatter(Data_Cord_Placenta_ITU, x = "EAAR_Bohlin", y = "EAAR_Lee", 
          add = "reg.line", conf.int = TRUE, 
          xlab = "EAAR Cord blood", ylab = "EAAR fetal Placenta")+
          #stat_cor(method = "pearson", label.x = -2.5, label.y = 5, r.digits = 1, p.digits = 3)+
          geom_hline(yintercept=0, linetype="dashed")+
          geom_vline(xintercept=0, linetype="dashed")+
        theme(text = element_text(size=13), axis.text.x = element_text(size=13))+
          coord_cartesian(xlim = c(-2, 2), ylim=c(-4,4)) 
  #scale_y_continuous(breaks = c(-4,-3,-2,-1,0,1,2,3,4)) +
  #scale_x_continuous(breaks = c(-2,-1,0,1,2))

resid_cordplacenta_itu <- na.omit(Data_Cord_Placenta_ITU[ ,c("Sample_Name", "EAAR_Bohlin", "EAAR_Lee")])
resid_cordplacenta_itu$EAAR_Bohlin_s <- scale(resid_cordplacenta_itu$EAAR_Bohlin)
resid_cordplacenta_itu$EAAR_Lee_s <- scale(resid_cordplacenta_itu$EAAR_Lee)
names(resid_cordplacenta_itu) <- c("Sample_Name", "Cord blood", "Placenta", "EAAR Cord blood (scaled)", "EAAR Placenta (scaled)")
resid_cordplacenta_itu_ls = reshape2::melt(resid_cordplacenta_itu[ ,c(1:3)])
col_resid_cordplacenta_itu_ls <- factor(resid_cordplacenta_itu_ls$Sample_Name)

color_plot = grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), invert = T)]
color_plot <- color_plot[1:363]

box_cord_placenta_resid <- ggplot(data=resid_cordplacenta_itu_ls, aes(x=variable, y=value))+
  geom_boxplot()+
  #geom_point(aes(colour = col_resid_cordplacenta_itu_ls))+
  geom_jitter(aes(colour = col_resid_cordplacenta_itu_ls), size=0.4, alpha=0.9)+
  scale_color_manual(values=color_plot)+
  ylab("epigenetic age acceleration residuals")+ 
  xlab("")+
  theme(legend.position = "none") 

box_cord_placenta_resid

png(filename="Results/Figures/diffTissues/corEAAR_cord_placenta_ITU.png", width=2600, height=1600, res=500)
cor_cord_placenta_resid
dev.off()

png(filename="Results/Figures/diffTissues/corEAAR_cord_placenta_ITU_F.png", width=2600, height=1600, res=500)
cor_cord_placenta_resid_f
dev.off()

png(filename="Results/Figures/diffTissues/boxEAAR_cord_placenta_ITU.png", width=2800, height=1400, res=400)
box_cord_placenta_resid 
dev.off()

#levenes test 
leveneTest(value ~ variable, resid_cordplacenta_itu_ls, center=mean)
# significant
#Levene's Test for Homogeneity of Variance (center = mean)
#       Df F value    Pr(>F)    
#group   1  135.76 < 2.2e-16 ***
#      724
```

```{r}
# paired t-test
d <- with(resid_cordplacenta_itu_ls, 
        value[variable == "Cord blood"] - value[variable == "Placenta"])
# Shapiro-Wilk normality test for the differences
shapiro.test(d)
# distribution of the differences (d) are not significantly different from normal distribution. In other words, we can assume the normality

t_paired_itu_resid <- t.test(value ~ variable, data = resid_cordplacenta_itu_ls, paired = TRUE)
t_paired_itu_resid
tidy_t_paired_itu_resid <- broom::tidy(t_paired_itu_resid)

ddply(resid_cordplacenta_itu_ls, .(variable), colwise(mean))
ddply(resid_cordplacenta_itu_ls, .(variable), colwise(sd))


write.csv(tidy_t_paired_itu_resid, "Results/Tables/t_paired_eaar_itu_cordplacenta.csv")
```

*Cord blood and Placenta (in PREDO)*
```{r}
DNAmGAResidCPREDO <- Data_PREDO_EPIC_Cord_Placenta[ ,c("EAAR_Bohlin","EAAR_Lee")]
colnames(DNAmGAResidCPREDO) <- c("Cordblood", "Placenta")
```

```{r}
allcorrsDNAmGAResidCPREDO <- rcorr(as.matrix(DNAmGAResidCPREDO))
allcorrsDNAmGAResidCPREDO
```


```{r}
cor_cord_placenta_resid_predo <- ggscatter(Data_PREDO_EPIC_Cord_Placenta, x = "EAAR_Bohlin", y = "EAAR_Lee", 
          add = "reg.line", conf.int = TRUE, 
          xlab = "EAAR Cord blood", ylab = "EAAR decidual Placenta")+
          stat_cor(method = "pearson", label.x = -2, label.y = 4, r.digits = 1, p.digits = 2)+
          geom_hline(yintercept=0, linetype="dashed")+
          geom_vline(xintercept=0, linetype="dashed")+
        theme(text = element_text(size=13), axis.text.x = element_text(size=13))+
          coord_cartesian(xlim = c(-2, 2), ylim=c(-4,4))

cor_cord_placenta_resid_predo

cor_cord_placenta_resid_predo_f <- ggscatter(Data_PREDO_EPIC_Cord_Placenta, x = "EAAR_Bohlin", y = "EAAR_Lee", 
          add = "reg.line", conf.int = TRUE, 
          xlab = "EAAR Cord blood", ylab = "EAAR decidual Placenta")+
          geom_hline(yintercept=0, linetype="dashed")+
          geom_vline(xintercept=0, linetype="dashed")+
        theme(text = element_text(size=13), axis.text.x = element_text(size=13))+
          coord_cartesian(xlim = c(-2, 2), ylim=c(-4,4))

cor_cord_placenta_resid_predo_f

resid_cordplacenta_predo <- na.omit(Data_PREDO_EPIC_Cord_Placenta[ ,c("Sample_Name", "EAAR_Bohlin", "EAAR_Lee")])
resid_cordplacenta_predo$EAAR_Bohlin_s <- scale(resid_cordplacenta_predo$EAAR_Bohlin)
resid_cordplacenta_predo$EAAR_Lee_s <- scale(resid_cordplacenta_predo$EAAR_Lee)
names(resid_cordplacenta_predo) <- c("Sample_Name", "Cord blood", "Placenta", "EAAR Cord blood (scaled)", "EAAR Placenta (scaled)")
resid_cordplacenta_predo_ls = reshape2::melt(resid_cordplacenta_predo[ ,c(1:3)])
col_resid_cordplacenta_predo_ls <- factor(resid_cordplacenta_predo_ls$Sample_Name)

color_plot = grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), invert = T)]
color_plot <- color_plot[1:116]

box_cord_placenta_resid_predo <- ggplot(data=resid_cordplacenta_predo_ls, aes(x=variable, y=value))+
  geom_boxplot()+
  #geom_point(aes(colour = col_resid_cordplacenta_itu_ls))+
  geom_jitter(aes(colour = col_resid_cordplacenta_predo_ls), size=0.4, alpha=0.9)+
  scale_color_manual(values=color_plot)+
  ylab("epigenetic age acceleration residuals")+ 
  theme(legend.position = "none") 

box_cord_placenta_resid_predo

png(filename="Results/Figures/diffTissues/corEAAR_cord_placenta_PREDO.png", width=2600, height=1600, res=500)
cor_cord_placenta_resid_predo
dev.off()

png(filename="Results/Figures/diffTissues/corEAAR_cord_placenta_PREDO_F.png", width=2600, height=1600, res=500)
cor_cord_placenta_resid_predo_f
dev.off()

png(filename="Results/Figures/diffTissues/boxEAAR_cord_placenta_PREDO.png", width=2800, height=1400, res=400)
box_cord_placenta_resid_predo
dev.off()

#levenes test 
leveneTest(value ~ variable, resid_cordplacenta_predo_ls, center=mean)
# significant
```

```{r}
# paired t-test
d <- with(resid_cordplacenta_predo_ls, 
        value[variable == "Cord blood"] - value[variable == "Placenta"])
# Shapiro-Wilk normality test for the differences
shapiro.test(d)
# distribution of the differences (d) are not significantly different from normal distribution. In other words, we can assume the normality

t_paired_predo_resid <- t.test(value ~ variable, data = resid_cordplacenta_predo_ls, paired = TRUE)
tidy_t_paired_predo_resid <- broom::tidy(t_paired_predo_resid)

write.csv(tidy_t_paired_predo_resid, "Results/Tables/t_paired_eaar_predo_cordplacenta.csv")

t_paired_predo_resid
ddply(resid_cordplacenta_predo_ls, .(variable), colwise(mean))
ddply(resid_cordplacenta_predo_ls, .(variable), colwise(sd))
```


*CVS and Placenta*
```{r}
DNAmGAResidCCP <- Data_CVS_Placenta_ITU[ ,c("EAAR_Lee_CVS", "EAAR_Lee_Placenta")]
colnames(DNAmGAResidCCP) <- c("CVS", "Placenta")
```

```{r}
allcorrsDNAmGAResidCCP <- rcorr(as.matrix(DNAmGAResidCCP))
allcorrsDNAmGAResidCCP
```


```{r}
cor_cvs_placenta_resid <- ggscatter(Data_CVS_Placenta_ITU, x = "EAAR_Lee_CVS", y = "EAAR_Lee_Placenta", 
          add = "reg.line", conf.int = TRUE, xlab = "EAAR CVS", ylab = "EAAR fetal Placenta")+
         theme(text = element_text(size=13), axis.text.x = element_text(size=13))+
         coord_cartesian(xlim = c(-2, 2), ylim=c(-4,4))+
          stat_cor(method = "pearson", label.x = -2, label.y = 4, r.digits = 2, p.digits = 2)+
          geom_hline(yintercept=0, linetype="dashed")+
          geom_vline(xintercept=0, linetype="dashed")

cor_cvs_placenta_resid

cor_cvs_placenta_resid_f <- ggscatter(Data_CVS_Placenta_ITU, x = "EAAR_Lee_CVS", y = "EAAR_Lee_Placenta", 
          add = "reg.line", conf.int = TRUE, xlab = "EAAR CVS", ylab = "EAAR fetal Placenta")+
          geom_hline(yintercept=0, linetype="dashed")+
          geom_vline(xintercept=0, linetype="dashed")+
        theme(text = element_text(size=13), axis.text.x = element_text(size=13))+
         coord_cartesian(xlim = c(-2, 2), ylim=c(-4,4))

cor_cvs_placenta_resid_f

resid_cvsplacenta_itu <- na.omit(Data_CVS_Placenta_ITU[ ,c("Sample_Name", "EAAR_Lee_CVS", "EAAR_Lee_Placenta")])
resid_cvsplacenta_itu$EAAR_Bohlin_s <- scale(resid_cvsplacenta_itu$EAAR_Lee_CVS)
resid_cvsplacenta_itu$EAAR_Lee_s <- scale(resid_cvsplacenta_itu$EAAR_Lee_Placenta)
names(resid_cvsplacenta_itu) <- c("Sample_Name", "CVS", "Placenta", "EAAR CVS (scaled)", "EAAR Placenta (scaled)")
resid_cvsplacenta_itu_ls = reshape2::melt(resid_cvsplacenta_itu[ ,c(1:3)])
col_resid_cvsplacenta_itu_ls <- factor(resid_cvsplacenta_itu_ls$Sample_Name)

color_plot = grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), invert = T)]
color_plot <- color_plot[1:78]

box_cvs_placenta_resid <- ggplot(data=resid_cvsplacenta_itu_ls, aes(x=variable, y=value))+
  geom_boxplot() +
  #geom_point(aes(colour = col_resid_cordplacenta_itu_ls))+
  geom_jitter(aes(colour = col_resid_cvsplacenta_itu_ls), size=0.4, alpha=0.9)+
  scale_color_manual(values=color_plot)+
  ylab("epigenetic age acceleration residuals")+ 
  xlab("")+
  theme(legend.position = "none") 

box_cvs_placenta_resid

png(filename="Results/Figures/diffTissues/corEAAR_cvs_placenta_ITU.png", width=2600, height=1600, res=500)
cor_cvs_placenta_resid
dev.off()

png(filename="Results/Figures/diffTissues/corEAAR_cvs_placenta_ITU_F.png", width=2600, height= 1600, res=500)
cor_cvs_placenta_resid_f
dev.off()

png(filename="Results/Figures/diffTissues/boxEAAR_cvs_placenta_ITU.png", width=2800, height=1400, res=400)
box_cvs_placenta_resid 
dev.off()

# test if variance in EAAR differes between cvs & placenta using levenes test
leveneTest(value ~ variable, resid_cvsplacenta_itu_ls, center=mean)
# not significant
```

```{r}
# paired t-test
d <- with(resid_cvsplacenta_itu_ls, 
        value[variable == "CVS"] - value[variable == "Placenta"])
# Shapiro-Wilk normality test for the differences
shapiro.test(d)
# distribution of the differences (d) are significantly different from normal

t_paired_itu_cvsplacenta_resid <- t.test(value ~ variable, data = resid_cvsplacenta_itu_ls, paired = TRUE)
tidy_t_paired_itu_cvsplacenta_resid <- broom::tidy(t_paired_itu_cvsplacenta_resid)

t_paired_itu_cvsplacenta_resid

write.csv(tidy_t_paired_itu_cvsplacenta_resid, "Results/Tables/t_paired_itu_eaar_cvsplacenta.csv")

ddply(resid_cvsplacenta_itu_ls, .(variable), colwise(mean))
ddply(resid_cvsplacenta_itu_ls, .(variable), colwise(sd))
```

*CVS and Cord blood*
```{r}
DNAmGAResidCC <- Data_CVS_Cord_ITU[ ,c("EAAR_Lee", "EAAR_Bohlin")]
colnames(DNAmGAResidCC) <- c("CVS", "Cord")
```

```{r}
allcorrsDNAmGAResidCC <- rcorr(as.matrix(DNAmGAResidCC))
allcorrsDNAmGAResidCC
```

```{r}
cor_cvs_cord_resid <- ggscatter(Data_CVS_Cord_ITU, x = "EAAR_Lee", y = "EAAR_Bohlin", 
          add = "reg.line", conf.int = TRUE, xlab = "EAAR CVS", ylab = "EAAR Cord blood")+
          stat_cor(method = "pearson", label.x = -2, label.y = 2, r.digits = 2, p.digits = 2)+
          geom_hline(yintercept=0, linetype="dashed")+
          geom_vline(xintercept=0, linetype="dashed")+
  theme(text = element_text(size=13), axis.text.x = element_text(size=13))+
          coord_cartesian(xlim = c(-2, 2), ylim=c(-2,2))

cor_cvs_cord_resid

cor_cvs_cord_resid_f <- ggscatter(Data_CVS_Cord_ITU, x = "EAAR_Lee", y = "EAAR_Bohlin", 
          add = "reg.line", conf.int = TRUE, xlab = "EAAR CVS", ylab = "EAAR Cord blood")+
          geom_hline(yintercept=0, linetype="dashed")+
          geom_vline(xintercept=0, linetype="dashed")+
  theme(text = element_text(size=13), axis.text.x = element_text(size=13))+
          coord_cartesian(xlim = c(-2, 2), ylim=c(-2,2))

cor_cvs_cord_resid_f

resid_cvscord_itu <- na.omit(Data_CVS_Cord_ITU[ ,c("Sample_Name", "EAAR_Bohlin", "EAAR_Lee")])
resid_cvscord_itu$EAAR_Bohlin_s <- scale(resid_cvscord_itu$EAAR_Bohlin)
resid_cvscord_itu$EAAR_Lee_s <- scale(resid_cvscord_itu$EAAR_Lee)
names(resid_cvscord_itu) <- c("Sample_Name", "Cord blood", "CVS", "EAAR Cord blood (scaled)", "EAAR CVS (scaled)")
resid_cvscord_itu_ls = reshape2::melt(resid_cvscord_itu[ ,c(1:3)])
col_resid_cvscord_itu_ls <- factor(resid_cvscord_itu_ls$Sample_Name)

color_plot = grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), invert = T)]
color_plot <- color_plot[1:363]

box_cvs_cord_resid <- ggplot(data=resid_cvscord_itu_ls, aes(x=variable, y=value))+
  geom_boxplot()+
  #geom_point(aes(colour = col_resid_cordplacenta_itu_ls))+
  geom_jitter(aes(colour = col_resid_cvscord_itu_ls), size=0.4, alpha=0.9)+
  scale_color_manual(values=color_plot)+
  ylab("epigenetic age acceleration residuals")+ 
  xlab("")+
  theme(legend.position = "none") 

box_cvs_cord_resid

png(filename="Results/Figures/diffTissues/corEAAR_cvs_cord_ITU.png", width=2600, height=1600, res=500)
cor_cvs_cord_resid
dev.off()

png(filename="Results/Figures/diffTissues/corEAAR_cvs_cord_ITU_F.png", width=2600, height=1600, res=500)
cor_cvs_cord_resid_f
dev.off()

png(filename="Results/Figures/diffTissues/boxEAAR_cvs_cord_ITU.png", width=2800, height=1400, res=400)
box_cvs_cord_resid 
dev.off()

#levenes test 
leveneTest(value ~ variable, resid_cvscord_itu_ls, center=mean)
# significant
# Levene's Test for Homogeneity of Variance (center = mean)
#        Df F value    Pr(>F)    
# group   1   14.13 0.0002567 ***
#       130   
```

```{r}
# paired t-test
d <- with(resid_cvscord_itu_ls, 
        value[variable == "CVS"] - value[variable == "Cord blood"])
# Shapiro-Wilk normality test for the differences
shapiro.test(d)
# distribution of the differences (d) are significantly different from normal

t_paired_itu_cvscord_resid <- t.test(value ~ variable, data = resid_cvscord_itu_ls, paired = TRUE)
tidy_t_paired_itu_cvscord_resid <- broom::tidy(t_paired_itu_cvscord_resid)

wilc_paired_itu_cvscord_resid <- wilcox.test(value ~ variable, data = resid_cvscord_itu_ls, paired = TRUE)
qnorm(wilc_paired_itu_cvscord_resid$p.value/2)
wilcoxonZ(resid_cvscord_itu$`Cord blood`, resid_cvscord_itu$CVS, paired = TRUE)
tidy_wilc_paired_itu_cvscord_resid <- broom::tidy(wilc_paired_itu_cvscord_resid)

write.csv(tidy_t_paired_itu_cvscord_resid, "Results/Tables/t_paired_itu_eaar_cvscord_resid.csv")
write.csv(tidy_wilc_paired_itu_cvscord_resid, "Results/Tables/wilc_paired_itu_eaar_cvscord_resid.csv")

wilc_paired_itu_cvscord_resid
ddply(resid_cvscord_itu_ls, .(variable), colwise(mean))
ddply(resid_cvscord_itu_ls, .(variable), colwise(sd))
```

```{r}
png(filename="Results/Figures/diffTissues/EAAR_correlations_tissues.png", width=3000, height=2000, res=300)
gridExtra::grid.arrange(cor_cvs_placenta_resid, cor_cvs_cord_resid, cor_cord_placenta_resid, cor_cord_placenta_resid_predo, ncol = 2)
dev.off()
```

[to the top](#top) 

## Difference in EAAR between Tissues {#DifferenceEAARTissues}  

*individuals with data from cordblood + placenta -ITU *
```{r}
# difference between cordblood and placenta
Data_Cord_Placenta_ITU$differenceEAAR <- Data_Cord_Placenta_ITU$EAAR_Bohlin - Data_Cord_Placenta_ITU$EAAR_Lee
#n=390
```


```{r}
# What is the absolute difference between cordblood and placenta?
Data_Cord_Placenta_ITU$absdifferenceEAAR <- abs(Data_Cord_Placenta_ITU$EAAR_Bohlin - Data_Cord_Placenta_ITU$EAAR_Lee)
```


```{r}
box_abs_resid_ITU <- ggplot(Data_Cord_Placenta_ITU, aes(x =Child_Sex, y = absdifferenceEAAR)) +
  geom_boxplot() +
  labs(x="child sex", y="absolute difference between EAARs", title="ITU")

melt_Data_Cord_Placenta_ITU <- reshape2::melt(Data_Cord_Placenta_ITU[ ,c("EAAR_Bohlin", "EAAR_Lee")])

box_EAAR_cordplacenta_ITU <- ggplot(melt_Data_Cord_Placenta_ITU, aes(x =factor(variable), y = value)) +
  geom_boxplot() +
  labs(x="", y="EAAR")+
  scale_x_discrete(labels = c('cord blood','placenta'))

hists_abs_resid_ITU <- ggplot(Data_Cord_Placenta_ITU, aes(x=absdifferenceEAAR))+ 
  geom_histogram(bins=58)+
  scale_x_continuous(breaks=c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5))+
  labs(x="absolute difference betweeen EAARs \n(cord blood vs. placenta)",y="Count (n = 363)")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


hists_resid_ITU <- ggplot(Data_Cord_Placenta_ITU, aes(x=differenceEAAR))+ 
  geom_histogram(bins=58)+
  scale_x_continuous(breaks=c(-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5))+
  labs(x="Cord blood - fetal Placenta (EAARs)", y = "Count (n = 363)")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


grid.arrange(box_abs_resid_ITU, hists_abs_resid_ITU, ncol=2)

median(Data_Cord_Placenta_ITU$absdifferenceEAAR, na.rm=T)

box_EAAR_cordplacenta_ITU

hists_resid_ITU

```

*individuals with data from cord blood and placenta - PREDO*
```{r}
# difference between cordblood and placenta
Data_PREDO_EPIC_Cord_Placenta$differenceEAAR <- Data_PREDO_EPIC_Cord_Placenta$EAAR_Bohlin - Data_PREDO_EPIC_Cord_Placenta$EAAR_Lee
```
<div class="alert alert-info">
* variable differenceresidualGAC = residual GA for cordblood minus residual GA for placenta (residual from DNAmGA~GA)    
</div>

```{r}
# What is the absolute difference between cordblood and placenta?
Data_PREDO_EPIC_Cord_Placenta$absdifferenceEAAR <- abs(Data_PREDO_EPIC_Cord_Placenta$EAAR_Bohlin - Data_PREDO_EPIC_Cord_Placenta$EAAR_Lee)
```
<div class="alert alert-info">
* variable absdifferenceresidualGAC = absolute difference between residual GA for cordblood vs placenta
</div>

```{r}
box_abs_resid_PREDO <- ggplot(Data_PREDO_EPIC_Cord_Placenta, aes(x =Child_Sex, y = absdifferenceEAAR)) +
  geom_boxplot() +
  labs(x="child sex", y="absolute difference between EAARs", title="PREDO")

hists_abs_resid_PREDO <- ggplot(Data_PREDO_EPIC_Cord_Placenta, aes(x=absdifferenceEAAR))+ 
  geom_histogram(bins=58)+
  scale_x_continuous(breaks=c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5))+
  labs(x="absolute difference betweeen EAARs \n(cord blood vs. placenta)",y="Count (n = 116)")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


grid.arrange(box_abs_resid_PREDO, hists_abs_resid_PREDO, ncol=2)

median(Data_PREDO_EPIC_Cord_Placenta$absdifferenceEAAR, na.rm=T)

hists_resid_PREDO <- ggplot(Data_PREDO_EPIC_Cord_Placenta, aes(x=differenceEAAR))+ 
  geom_histogram(bins=58)+
  scale_x_continuous(breaks=c(-3,-2,-1,0,1,2,3,4))+
  labs(x="Cord blood - decidual Placenta (EAARs)", y="Count (n = 116)")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))

  

hists_resid_PREDO
```


*individuals with data from cvs + cordblood*  
```{r}
# difference between cvs and cordblood 
Data_CVS_Cord_ITU$differenceEAAR <- Data_CVS_Cord_ITU$EAAR_Lee - Data_CVS_Cord_ITU$EAAR_Bohlin
#n=73
```

```{r}
# What is the absolute difference between cordblood and placenta?
Data_CVS_Cord_ITU$absdifferenceEAAR <- abs(Data_CVS_Cord_ITU$EAAR_Lee - Data_CVS_Cord_ITU$EAAR_Bohlin)
```

```{r}
box_abs_resid_ITU_cc <- ggplot(Data_CVS_Cord_ITU, aes(x =Child_Sex, y = absdifferenceEAAR)) +
  geom_boxplot() +
  labs(x="child sex", y="absolute difference between EAARs", title="ITU")

melt_Data_CVS_Cord_ITU <- reshape2::melt(Data_CVS_Cord_ITU[ ,c("EAAR_Bohlin", "EAAR_Lee")])

box_EAAR_cvscord_ITU <- ggplot(melt_Data_CVS_Cord_ITU, aes(x =factor(variable), y = value)) +
  geom_boxplot() +
  labs(x="", y="EAAR")
  #scale_x_discrete(labels = c('cord blood','placenta'))

hists_abs_resid_ITU_cc <- ggplot(Data_CVS_Cord_ITU, aes(x=absdifferenceEAAR))+ 
  geom_histogram(bins=58)+
  labs(x="absolute difference betweeen EAARs \n(cord blood vs. placenta)",y="Count (n = 66)")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


hists_resid_ITU_cc <- ggplot(Data_CVS_Cord_ITU, aes(x=differenceEAAR))+ 
  geom_histogram(bins=58)+
  coord_cartesian(xlim = c(-4, 4))+
  #scale_x_continuous(limits = c(-4, 4))+
  scale_x_continuous(breaks=c(-4,-3, -2, -1, 0, 1, 2, 3,4))+
  labs(x="CVS - Cord blood (EAARs)", y = "Count (n = 66)")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


grid.arrange(box_abs_resid_ITU_cc, hists_abs_resid_ITU_cc, ncol=2)

median(Data_CVS_Cord_ITU$absdifferenceEAAR, na.rm=T)

box_EAAR_cvscord_ITU

hists_resid_ITU_cc

```

*individuals with data from cvs + placenta*  
```{r}
# difference between cvs and placenta 
Data_CVS_Placenta_ITU$differenceEAAR <- Data_CVS_Placenta_ITU$EAAR_Lee_CVS - Data_CVS_Placenta_ITU$EAAR_Lee_Placenta
#n=86
```

```{r}
# What is the absolute difference between cordblood and placenta?
Data_CVS_Placenta_ITU$absdifferenceEAAR <- abs(Data_CVS_Placenta_ITU$EAAR_Lee_CVS - Data_CVS_Placenta_ITU$EAAR_Lee_Placenta)
```

```{r}
box_abs_resid_ITU_cp <- ggplot(Data_CVS_Placenta_ITU, aes(x =Child_Sex, y = absdifferenceEAAR)) +
  geom_boxplot() +
  labs(x="child sex", y="absolute difference between EAARs", title="ITU")

melt_Data_CVS_Placenta_ITU <- reshape2::melt(Data_CVS_Placenta_ITU[ ,c("EAAR_Lee_CVS", "EAAR_Lee_Placenta")])

box_EAAR_cvsplacenta_ITU <- ggplot(melt_Data_CVS_Placenta_ITU, aes(x =factor(variable), y = value)) +
  geom_boxplot() +
  labs(x="", y="EAAR")
  #scale_x_discrete(labels = c('cord blood','placenta'))

hists_abs_resid_ITU_cp <- ggplot(Data_CVS_Placenta_ITU, aes(x=absdifferenceEAAR))+ 
  geom_histogram(bins=58)+
  scale_x_continuous(breaks=c(0,0.5,1,1.5,2,2.5,3,3.5,4,4.5,5))+
  labs(x="absolute difference betweeen EAARs \n(cord blood vs. placenta)",y="Count (n = 78)")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


hists_resid_ITU_cp <- ggplot(Data_CVS_Placenta_ITU, aes(x=differenceEAAR))+ 
  geom_histogram(bins=58)+
  coord_cartesian(xlim = c(-3, 3))+
  #scale_x_continuous(limits = c(-4, 4))+
  scale_x_continuous(breaks=c(-3, -2, -1, 0, 1, 2, 3))+
  labs(x="CVS - fetal Placenta (EAARs)", y = "Count (n = 78)")+
  theme_bw()+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))


grid.arrange(box_abs_resid_ITU_cp, hists_abs_resid_ITU_cp, ncol=2)

median(Data_CVS_Placenta_ITU$absdifferenceEAAR, na.rm=T)

box_EAAR_cvsplacenta_ITU

hists_resid_ITU_cp

```

*individuals with data from cvs + cordblood + placenta*

```{r}
resid_Data_Full_ITU <- na.omit(Data_Full_ITU[ ,c("Sample_Name", "EAAR_Bohlin", "EAAR_Lee_CVS", "EAAR_Lee_Placenta")]) #60
resid_Data_Full_ITU_z <- resid_Data_Full_ITU[ ,c("Sample_Name", "EAAR_Bohlin", "EAAR_Lee_CVS", "EAAR_Lee_Placenta")]

resid_Data_Full_ITU$`Cord blood` <- resid_Data_Full_ITU$EAAR_Bohlin
resid_Data_Full_ITU$CVS <- resid_Data_Full_ITU$EAAR_Lee_CVS
resid_Data_Full_ITU$`Placenta (fetal)` <- resid_Data_Full_ITU$EAAR_Lee_Placenta
resid_Data_Full_ITU$EAAR_Bohlin <- NULL
resid_Data_Full_ITU$EAAR_Lee_CVS <- NULL
resid_Data_Full_ITU$EAAR_Lee_Placenta <- NULL

resid_Data_Full_ITU_z$`Cord blood` <- scale(resid_Data_Full_ITU_z$EAAR_Bohlin)
resid_Data_Full_ITU_z$CVS <- scale(resid_Data_Full_ITU_z$EAAR_Lee_CVS)
resid_Data_Full_ITU_z$`Placenta (fetal)` <- scale(resid_Data_Full_ITU_z$EAAR_Lee_Placenta)
resid_Data_Full_ITU_z$EAAR_Bohlin <- NULL
resid_Data_Full_ITU_z$EAAR_Lee_CVS <- NULL
resid_Data_Full_ITU_z$EAAR_Lee_Placenta <- NULL
```

```{r}
long_resid_Data_Full_ITU_z <- melt(as.data.table(resid_Data_Full_ITU_z), id.vars = "Sample_Name", variable.name = "sampling")
long_resid_Data_Full_ITU_z$sampling <- factor(long_resid_Data_Full_ITU_z$sampling, levels = c("CVS", "Placenta (fetal)", "Cord blood"))
```

```{r}
long_resid_Data_Full_ITU <- melt(as.data.table(resid_Data_Full_ITU), id.vars = "Sample_Name", variable.name = "sampling")
long_resid_Data_Full_ITU$sampling <- factor(long_resid_Data_Full_ITU$sampling, levels = c("CVS", "Placenta (fetal)", "Cord blood"))
```

```{r}
library(randomcoloR)
n <- 60
palette <- distinctColorPalette(n)
```

*Plots*
```{r}
ggplot(long_resid_Data_Full_ITU_z, aes(x=sampling, y=value, group=as.factor(Sample_Name), color=as.factor(Sample_Name))) + 
  geom_point()+
  geom_line()+
  scale_color_manual(values=palette)+
  theme_bw()+
  theme(legend.position = "none")+
  theme(text = element_text(size = 15), axis.title.x= element_text(size=15), axis.title.y= element_text(size=15))+
  labs(x="", y = "z-standardized EAAR")
```

```{r}
ggplot(long_resid_Data_Full_ITU, aes(x=sampling, y=value, group=as.factor(Sample_Name), color=as.factor(Sample_Name))) + 
  geom_point()+
  geom_line()+
  scale_color_manual(values=palette)+
  theme_bw()+
  theme(legend.position = "none")+
  theme(text = element_text(size = 15, color="black"), axis.title.x= element_text(size=15, color="black"), axis.title.y= element_text(size=15), axis.text.x=element_text(colour="black"))+
  labs(x="", y = "EAAR (n = 60)")
```

```{r}
png(file="Results/Figures/diffTissues/EAAR_CVSCordPlacenta_ITU.png", width=3000, height=1500, res=400)
ggplot(long_resid_Data_Full_ITU, aes(x=sampling, y=value, group=as.factor(Sample_Name), color=as.factor(Sample_Name))) + 
  geom_point()+
  geom_line()+
  scale_color_manual(values=palette)+
  theme_bw()+
  theme(legend.position = "none")+
  theme(text = element_text(size = 11, color="black"), axis.title.x= element_text(size=13, color="black"), axis.title.y= element_text(size=13), axis.text.x=element_text(size=13, colour="black"))+
  labs(x="", y = "EAAR (n = 60)")
dev.off()

```

```{r}
png(file="Results/Figures/diffTissues/EAAR_PlacentaCord_ITU.png", width=2500, height=1500, res=400)
hists_abs_resid_ITU
dev.off()

png(file="Results/Figures/diffTissues/EAAR_PlacentaCord_PREDO.png", width=2500, height=1500, res=400)
hists_abs_resid_PREDO
dev.off()

png(file="Results/Figures/diffTissues/EAAR_PlacentaCord.png", width=3500, height=1500, res=400)
grid.arrange(hists_abs_resid_ITU, hists_abs_resid_PREDO, ncol = 2)
dev.off()

png(file="Results/Figures/diffTissues/EAAR_diffCordPlacenta_ITU.png", width=2500, height=1500, res=400)
hists_resid_ITU
dev.off()

png(file="Results/Figures/diffTissues/EAAR_diffCordPlacenta_PREDO.png", width=2500, height=1500, res=400)
hists_resid_PREDO
dev.off()

png(file="Results/Figures/diffTissues/EAAR_diffCVSCord_ITU.png", width=2500, height=1500, res=400)
hists_resid_ITU_cc
dev.off()

png(file="Results/Figures/diffTissues/EAAR_diffPlacentaCVS_ITU.png", width=2500, height=1500, res=400)
hists_resid_ITU_cp
dev.off()
```

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